A system for controllable content summarization

Abstract

A method for generating a summary of a content item using one or more Large Language Models (LLMs) is disclosed. A first content item is identified. The first content item contains a set of sub-content items. The abstraction level of the content item is determined. A prompt is automatically generated to be provided to one or more LLMs. The prompt contains a reference to the first content item and the abstraction level of the first content item. A response to the prompt is received from the LLMs. The response contains a second content item. The second content item contains a representation of the first content item generated by the LLMs. The representation omits or simplifies one or more of the set of sub-content items based on the abstraction level. The representation is used to control the output to be communicated to a target device.

Description

Technical Field

[0001] This application claims priority to U.S. Patent Application No. 18 / 452,496 filed on August 18, 2023, which is incorporated herein by reference in its entirety.

[0002] The present application relates to artificial intelligence and machine learning systems, and more specifically, to systems and methods for summarizing content using large language models in a controlled and configurable manner.

Background Art

[0003] In recent years, artificial intelligence (AI) and machine learning (ML) technologies have advanced rapidly, leading to technical improvements in the understanding and generation of natural language content. Specifically, large-scale neural network models called large language models (LLMs) have demonstrated impressive capabilities in language understanding and text generation. LLMs such as GPT®-3, Codex, and others are capable of generating remarkably human-like text for a variety of applications.

Brief Description of the Drawings

[0004] Some embodiments are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings.

[0005] [Figure 1] It is a network diagram depicting a cloud-based SaaS system in which various exemplary embodiments may be deployed. [Figure 2] It is a block diagram illustrating exemplary modules of the content summarization service and / or application in FIG. 1. [Figure 3] It is a block diagram illustrating an exemplary method for generating an abstract summary of a content item using a large language model (LLM). [Figure 4A]This is a series of block diagrams depicting exemplary content, including chat, which is represented at multiple zoom levels in the user interface in response to user input. [Figure 4B] This is a series of block diagrams depicting exemplary content, including chat, which is represented at multiple zoom levels in the user interface in response to user input. [Figure 4C] This is a series of block diagrams depicting exemplary content, including chat, which is represented at multiple zoom levels in the user interface in response to user input. [Figure 4D] This is a series of block diagrams depicting exemplary content, including chat, which is represented at multiple zoom levels in the user interface in response to user input. [Figure 5A] This is a series of block diagrams depicting exemplary content, including legal documents, that are represented at multiple zoom levels within the user interface in response to user input. [Figure 5B] This is a series of block diagrams depicting exemplary content, including legal documents, that are represented at multiple zoom levels within the user interface in response to user input. [Figure 5C] This is a series of block diagrams depicting exemplary content, including legal documents, that are represented at multiple zoom levels within the user interface in response to user input. [Figure 5D] This is a series of block diagrams depicting exemplary content, including legal documents, that are represented at multiple zoom levels within the user interface in response to user input. [Figure 5E] This is a series of block diagrams depicting exemplary content, including legal documents, that are represented at multiple zoom levels within the user interface in response to user input. [Figure 6] This block diagram illustrates how a system can evolve its summarization performance over time. [Figure 7] This is a block diagram illustrating a mobile device according to an exemplary embodiment. [Figure 8] This is a block diagram of a machine in an exemplary form of a computer system in which instructions can be executed to cause the machine to perform any one or more of the techniques described herein. [Modes for carrying out the invention]

[0006] The following description includes numerous specific details to provide an understanding of various embodiments of the subject matter. However, it will be apparent to those skilled in the art that various embodiments can be carried out without these specific details.

[0007] Effectively leveraging LLMs to summarize long or complex content into more concise expressions remains a significant technical challenge. The core technical issue is controlling the level of abstraction and conciseness when generating abstract summaries. Existing approaches lack sufficient precision and user composability with respect to the depth of the summaries. Furthermore, iteratively refining summaries by re-providing the initial representation to the LLM for additional abstraction presents difficulties. Current systems also fail to preserve critical data when abbreviating content.

[0008] This disclosure provides specific technical solutions to these problems through one or more described features or combinations of features. The disclosed system allows users to precisely define the target level of abstraction for the representation of a summary of content items (for example, by specifying a percentage reduction in length). For example, the system provides fine-grained control with respect to depth and conciseness. The system may be configured to incorporate iterative prompts for the LLM to summarize its preceding output at a higher level of abstraction. This allows for stepwise refinement while preserving substantial information.

[0009] From a technical standpoint, these technological improvements enhance abstraction capabilities, provide configurable conciseness, enable iterative summarization, maintain data integrity, and ultimately advance cutting-edge technology. These improvements are rooted in specific technological aspects, including prompt engineering techniques, abstraction level control parameters, iterative LLM processing, and data retention during summarization.

[0010] This disclosure has specific technical significance given the challenges of summarization. Reducing the amount of information while maintaining quality is difficult, and pushing the limits of abstraction involves using granular AI / ML solutions. The disclosed technology leverages the expressive power of LLM in novel ways to achieve more general and controllable abstraction.

[0011] Technological advancements offer broad utility across information-intensive fields, including legal, academic, corporate, and governmental environments. By improving human-computer interaction in content summarization, these advancements also hold importance for accessibility and internationalization. The disclosed techniques are applicable to international systems and advance the field globally. Specific technological improvements enable more effective human information processing, knowledge management, and data understanding.

[0012] In some embodiments, the context-aware assistant is configured to scale, metaphorically, between two extremes: focusing on a forest or individual trees, where the forest represents a semantically scaled-out view of the interaction and the trees represent the explicit interaction. Scaling, zooming in, and zooming out are likely mapped to user interface elements or controls (e.g., ctrl + / - or cmd + / - for keyboard-equipped devices, finger pinch for touch screens, voice for voice interfaces, etc.).

[0013] A negative zoom scale may allow the system to infer from short, vague, or ambiguous text, and the system may reinforce the meaning of the dialogue using appropriate interrelated external data source signals (e.g., market data, crunchbase data). In an exemplary embodiment, a negative zoom scale may allow the system to infer and reinforce the summary with additional content. As the zoom percentage becomes more negative, the amount of additional content increases proportionally. For example, at -10% zoom, 10% more content may be added; at -50% zoom, 50% more content may be added, and so on. In an exemplary embodiment, the maximum amount of additional content may be set to 100% of the original content size (e.g., configurable size). In an exemplary embodiment, as the negative zoom increases, inferences made with lower certainty may be added. For each inference, the system specifies a percentage confidence level (e.g., 0% to 100%). Inferences with a more general certainty are added at lower zoom levels. In an exemplary embodiment, higher negative zoom levels incorporate more speculative external data, such as older news articles, loosely related research papers, or financial data from past years that are not directly linked to the core content. In an exemplary embodiment, the inferences and external data, and / or their confidence levels, may be visually distinguishable and / or identified (e.g., using formatting such as italics, color coding, icons, or labeling) to allow consumers to easily distinguish and / or evaluate the additional content.

[0014] In an exemplary embodiment, the system is configured to store important information across scales while preserving its semantic content. For example, only a certain amount of non-substantial and irrelevant information is reduced across scales during zooming. The system may provide hints (e.g., audio or visual) that information has been reduced in the zoomed view, via a user interface, API notifications, etc.

[0015] This scaling is not only for chat dialogues, but also for any sequence of multiple participants across various media types, including text dialogues, document sequences, web pages, their revision histories or separate transition series, etc. By way of example, a text dialogue may include a single person's notes (to themselves), two conversing parties, or multiple parties.

[0016] Large language model (LLM) tools are associated with text chat, while other large model vector databases and models can be used in other media sequence analysis.

[0017] When a large language model such as ChatGPT is used, instructions are given to avoid removing important information during summarization, for example, by constructing or building an appropriate ChatGPT prompt.

[0018] In an exemplary embodiment, negative zoom causes the capture of data from constantly updated external signal groups, and can deliver clearer composite semantic content than when existing alone within a dialogue.

[0019] In an exemplary embodiment, the gist of the idea is semantic scaling of constant situational awareness led by language models, which can be useful in various contexts as described in more detail herein.

[0020] A method for generating an abstract summary of a content item using one or more Large Language Models (LLMs) is disclosed. A first content item is identified. The first content item contains a set of sub-content items. The abstraction level of the content item is determined. A prompt is automatically generated to be provided to one or more LLMs. The prompt contains a reference to the first content item and the abstraction level of the first content item. A response to the prompt is received from the LLMs. The response contains a second content item. The second content item contains a representation of the first content item generated by the LLMs. The representation omits or simplifies one or more of the set of sub-content items based on the abstraction level. The representation is used to control the output to be communicated to a target device.

[0021] The summarization techniques described can be useful across content types, including legal documents, academic papers, medical records, and news articles. Condensing these often long and complex documents into more concise summaries can improve comprehension and accessibility.

[0022] The technique described herein offers novel capabilities in leveraging large-scale language models and other AI for controllable content summarization. Compared to summarization techniques in the art, the disclosed technique, which constructs prompts using configurable abstraction parameters to control LLM summarization, represents a substantial technological improvement. For example, existing systems lack the ability for precise control with respect to the depth and conciseness of abstraction when generating summarization using large-scale neural network models. They also fail to retain important data when summarizing content. The disclosed systems and techniques address these shortcomings through prompt engineering techniques, abstraction level construction, iterative LLM processing, data retention during summarization, and the like, as described herein.

[0023] The systems described herein differ from conventional systems in that they directly specify the target compression ratio and level of abstraction in prompts to the LLM, and / or iteratively reformulate prompts based on previous outputs to gradually increase the level of abstraction. Existing solutions do not employ similar methods to control the abstraction process.

[0024] The disclosed system also preserves key entities and facts, even with high compression ratios, when summarizing. In contrast, previous approaches often lose important details when attempting deeper abstractions. The system's data retention ensures substantial information integrity.

[0025] In an exemplary embodiment, the system leverages machine learning techniques to optimize prompt engineering for precise abstraction control. The prompts provided to the large-scale language model include content-specific instructions designed to extract a desired level of summarization depth and conciseness from the model.

[0026] The system is pre-trained on a large dataset of {content, prompt, summary} triplets. The content includes documents of varying lengths and complexities. Prompts are crafted using different instructions, keywords, and text manipulation techniques to target different levels of abstraction. Summaries are generated by humans at the target level of abstraction.

[0027] This training data allows the system to learn prompt engineering strategies adapted to different abstraction objectives. The neural network learns optimal prompt phrasing, text preprocessing such as entity masking, and prompt structure to achieve the target compression ratio.

[0028] For example, a prompt requiring high abstraction might focus on dropping less relevant sentences and emphasizing the overarching theme. A prompt for lower abstraction might specify the inclusion of important details and statistics.

[0029] At runtime, the trained prompt engineering model dynamically constructs prompts customized to the user-specified level of abstraction. A feedback loop continuously refines the prompts during use by analyzing the accuracy of the abstraction.

[0030] This machine learning approach allows for significantly greater control over the depth and conciseness of summaries compared to hardcoded rules or templates. The system has the flexibility to adapt prompts to the nuances of specific content using learned prompt engineering strategies.

[0031] The systems described herein offer substantial technical improvements to existing approaches to text summarization, including in areas such as abstraction control, iterative refinement, and / or information retention. Furthermore, rigorous quantitative and / or qualitative evaluations across datasets and metrics can be used to verify the systems' capabilities for high fidelity, consistency, and configurable abstraction control, as described herein.

[0032] Figure 1 is a network diagram depicting a system 100 in which various exemplary embodiments can be deployed. A networked system 102 of an exemplary form of cloud computing service, such as Microsoft Azure® or other cloud services, provides server-side functionality to one or more endpoints (e.g., client machines 110) over a network 104 (e.g., the Internet or a wide area network (WAN)). Figure 1 illustrates a client application 112 on a client machine 110. Examples of client applications 112 may include, as described herein, a content summarization application having controls for accessing content at different zoom levels, a management application (e.g., for configuring any of the configurable aspects of the content summarization application 120 described herein), a web browser application such as the Internet Explorer® browser developed by Microsoft Corporation in Redmond, Washington, or other applications supported by the device's operating system, such as applications supported by the Windows®, iOS®, or Android® operating systems. Each of the client applications 112 may include software application modules (e.g., plug-ins, add-ins, or macros) that add specific services or features to the application.

[0033] The application programming interface (API) server 114 and the web server 116 are coupled to a content summarization service 120, which may be hosted on a Software-as-a-Service (SaaS) layer or platform 104, providing programmatic and web interfaces, respectively. The SaaS platform may also be part of a service-oriented architecture, stacked on a Platform-as-a-Service (PaaS) layer 106 (for example, according to standards defined by NIST (National Institute of Standards and Technology)), and this PaaS layer may be stacked on an Infrastructure-as-a-Service (IaaS) layer 108.

[0034] Although the content summarization application 120 is shown in Figure 1 as forming part of the networked system 102, in an alternative embodiment, the application 120 may be separate from the networked system 102 and form part of a distinct service.

[0035] Furthermore, while the system 100 shown in Figure 1 employs a cloud-based architecture, various embodiments are, of course, not limited to such architecture and can be equally well applied to, for example, client-server, distributed, or peer-to-peer systems. The content summarization service 120 can also be implemented as a standalone software program.

[0036] A web application running on client machine 110 can access the content summarization service 120 via a web interface supported by web server 116. Similarly, a native application running on client machine 110 can access the service 120 via a programmatic interface provided by API server 114.

[0037] The content summarization service 120 may be hosted on dedicated or shared server machines that are communicatively coupled to enable communication between servers. The service 120 itself is communicatively coupled to various data sources, including content items 128 stored in the database 126 via the database server 124.

[0038] Navigation of content items 128 stored in the networked system 102 can be facilitated by one or more navigation applications. For example, a search application may enable keyword searching of content items 128. A browser application may enable the user to browse content items 128 according to attributes stored as metadata.

[0039] In some embodiments, when the content summarization service 120 is operating in inference mode, for example, when the maximum abstraction level threshold is exceeded, the service 120 may access external systems and data sources. For example, as illustrated in Figure 1, an external data store 130 containing supplementary information may be accessed via the network 104. These external data stores 130 may include knowledge bases, ontologs, public and private databases, and other structured data. Exemplary external data items include dictionaries, thesauruses, industry taxonomies, company and product databases, maps, scholarly paper repositories, legal document collections, and financial data. By leveraging these external data items in inference mode, the content summarization service 120 can enrich the representation of content item 128 to include additional contextual details and derived knowledge. This enables the service 120 to generate an abstract summary that goes beyond the information directly contained within content item 128.

[0040] The content summarization application 120 may be connected to one or more LLMs or other artificial intelligence machines 111. A Large-Scale Language Model (LLM) is a deep neural network trained on a large dataset of texts that, given a sequence of preceding words, predicts the most likely next word. LLMs like GPT-3 contain billions of parameters and are trained using self-supervised learning on an internet-sized corpus.

[0041] The architecture may include transformer blocks with an attention mechanism for modeling context and identifying highly relevant patterns across the entire input sequence. Attention weights and feedforward layers can transform input embeddings into more general, contextual representations used for word prediction.

[0042] During training, the model may be input sequentially with each text snippet and attempt to predict the next word using its current internal representation. The weights may be updated through backpropagation to minimize the prediction loss across the entire corpus.

[0043] Through numerous iterations across massive datasets, the network learns the complex statistical relationships and nuances of natural language to build a robust internal representation of linguistic context, grammar, semantics, and fluency.

[0044] The trained model can generate consistent, human-like text by iteratively sampling from its predicted next word distribution to continuously grow new sequences. The temperature parameter controls the balance between randomness and determinism in the sampling.

[0045] LLMs like GPT-3 expose their generation capabilities through APIs that allow them to send text prompts and receive model generation completion. The prompts can provide context and specify desired attributes for the output text.

[0046] The system can be configured to interact with the LLM API by programmatically constructing prompts designed to generate summaries with appropriate length, abstraction level, inference, and data integration (for example, based on zoom parameters).

[0047] The prompt can include the original text for summarization, along with instructions adapted to extract the target summary characteristics from the LLM. The system sends the prompt via the API and retrieves the summary generated by the LLM and presents it to the user.

[0048] Over time, the system can learn how different prompts affect the behavior of the LLM (e.g., through training on prompt-summary pairs). This enables the automatic generation of optimal prompts for each summarization task.

[0049] The system may be configured to leverage multiple LLMs with different capabilities, and the outputs can be combined using a compilation mechanism. Prompts and APIs enable the seamless integration of LLMs into the summarization workflow.

[0050] In addition to large-scale language models (LLMs), the system can leverage other types of AI to enhance its capabilities.

[0051] Extractive summarization models can identify and extract the most important sentences or phrases from the original text to generate a shortened summary. These models can be trained on text-summary pairs and learn to rank sentences based on importance. These models can use encoder-decoder architectures to convert sentences into vector representations and score them for relevance. The system can be configured to use an extractive summarization model to concatenate, for example, extracted snippets into a summary that satisfies length constraints.

[0052] Abstract summarization models can generate new phrases and sentences to compress meaning rather than directly extracting it. These can be trained to paraphrase and generalize concepts using a seq2seq architecture. Systems can be configured to use abstract summarization models to produce more fluent natural language summaries compared to extractive approaches, for example.

[0053] Question-answering (QA) models can address specific informational needs regarding text. They can be trained to take in clauses and questions and output short, excerpted answers. Pairs of QA models can be used for training data. A cascaded transformer architecture can handle the interactions between clauses, questions, and answers. The system can be configured to use the QA model to enrich the summary with requested details.

[0054] Data retrieval models can find and integrate highly relevant external data sources. These models may leverage reverse indexes, dense retrievers like DPRs, and knowledge graphs to identify contextually relevant data. The system can be configured to use data retrieval models to provide supplementary information to amplify summaries with external facts and context beyond the original text.

[0055] Classification models can classify text subjects for topic filtering and abstraction. Text classifiers can be trained on labeled document corpora to predict categories and tags. The system can be configured to use the classifier to orient summaries towards important topical aspects.

[0056] Sentiment analysis models determine emotional tone and opinion. These use techniques such as textblob polarity scoring and VADER emotional dictionary matching. The system can be configured to use the sentiment analysis model to customize, for example, the perspective and tone of a summary.

[0057] To integrate these other AI models, the system can use training loop and prompting techniques similar to those described for LLM. The model exposes a predictive API for the interface.

[0058] The system can construct prompts tailored to the capabilities of each model and combine the outputs for an ensemble summarization approach. The prompts provide text along with instructions designed to extract the desired analysis from each model.

[0059] For example, a single prompt could provide text to an LLM for an overall summary, ask a QA model for key details, have a classifier tag topics, have a retriever supplement with external data, and provide a sentiment analysis score tone.

[0060] The system manages the information flow between different models and aggregates their outputs into a comprehensive summary that satisfies specified parameters.

[0061] Over time, the system can learn how to optimally prompt each model and assemble an ensemble summary. The modular, replaceable architecture allows for the flexible integration of diverse AI capabilities beyond mere LLMs to enhance summarization capabilities.

[0062] The prompts and APIs abstract the underlying model implementations, enabling the system to organize and leverage an evolving ensemble of AI technologies as new models emerge. The system's model-independent approach facilitates experimentation and optimization to determine the best combination of models and prompts for each summarization task.

[0063] The data source machine 113 can provide data usable by various components of the system. Large-scale language models and other AI models integrated into the system can leverage diverse structured and unstructured data sources for their training and inference needs.

[0064] To pre-train the foundational model that the system will build, a vast corpus of text and other data may be used. This could include web crawl data, digitized books, Wikipedia, news archives, social media posts, discussion forums, and other text content extracted from websites and applications. The pre-training data may cover a wide range of topics, styles, and genres to develop diverse language capabilities.

[0065] During further training and fine-tuning of the model for the summarization task, a curated dataset of document-summary pairs in the target domain may be used. These provide examples for training the model to generate summaries with appropriate length, perspective, and / or other attributes.

[0066] To access external knowledge to supplement summaries, structured data sources such as knowledge graphs, databases, and API-accessible web services can provide relevant facts and context. These can include domain-specific data repositories, in addition to general knowledge bases like Wikidata. Prompts can specify the data needs to be targeted and the sources to be ingested.

[0067] Unstructured data can also provide supplemental training and inference information. For example, highly relevant news articles, financial reports, legal documents, and scientific papers can provide useful in-domain information. A pre-trained semantic search engine can help identify contextual text sections on a given topic.

[0068] Within the system itself, aggregated behavioral data from user interactions can also be used as feedback signals for ongoing training. This could include data on summaries that users find most useful, edits they make, zoom levels used, and other actions that reflect their subjective preferences.

[0069] By capturing and analyzing patterns of how real users interact with the system, personalized data is provided to tailor the model to each user's needs. The model can then learn to adapt prose style, perspective, reasoning, and external data ingestion.

[0070] To access diverse data sources, the system can leverage web crawlers, APIs, database integrations, and pre-built repositories. Data can be stored in cloud storage, data warehouses, and dedicated collections optimized for machine learning.

[0071] The prompts provide a flexible mechanism for specifying the dynamic data needs for each summarization task. The modularity of the system design allows for the expansion of data sources over time to improve model capabilities and training.

[0072] Figure 2 is a block diagram illustrating an exemplary module of the content summarization service 120.

[0073] The abstraction parameter module 210 allows configuration of abstraction parameters, such as the target compression percentage, via an API or user interface, to control the depth of the summary. The abstraction parameter module 210 may be configured to allow the user to precisely define the target abstraction level, for example, by selecting a specific compression percentage between 0 and 100%. Module 210 provides an interface that allows the user to numerically set the percentage of length reduction or manually select from predefined abstraction levels (e.g., high, medium, low). Default percentages may be set for different use cases.

[0074] The prompt engineering module 220 may be configured to construct prompts for the language model 230 using the original content. In one embodiment, the prompts are iteratively reconstructed based on the previous output to increase the level of abstraction. In an alternative embodiment, the original content is provided as input with different abstraction parameters, independent of the previous output. The prompt engineering module 220 may be configured to employ a variety of techniques to construct customized prompts for each different LLM 230. These techniques may include rearranging sentences to improve consistency, substituting named entities with placeholders to control the level of abstraction, inserting keywords and exemplary outputs to guide the LLM, and providing instructions that specify the target level of abstraction.

[0075] The language model module 230 may include or provide easy access to one or more pre-trained language models for generating abstract summaries. One or more models may be ready for immediate use or, optionally, be fine-tuned with domain-specific data.

[0076] The save module 240 saves important data and concepts from the original content during the summarization process.

[0077] The external data module 250 interfaces with external systems and data stores to capture content during inference mode when the abstraction threshold is exceeded.

[0078] The dialogue module 260 leverages summarization capabilities to guide the intelligent agent's conversation via the API.

[0079] The evaluation module 270 analyzes the summary output using metrics such as compression ratio, fidelity, consistency, and redundancy. Metrics and exemplary outputs can be presented via APIs and user interfaces for monitoring and refining the summarization process.

[0080] During operation, abstraction parameters can be configured via the abstraction parameter module 210 based on user or system requirements. The prompt engineering module 220 constructs customized prompts for each different language model 230. Summaries are generated iteratively, preserving key information and supplementing it with external data as needed. Metrics from the evaluation module 270 help guide continuous improvement of the summarization process. The evaluation module 270 may be configured to analyze the summary output using metrics including compression ratio, consistency, fidelity, and redundancy. Compression ratio measures the reduction in length between the original content and the summarized content. Consistency measures how logical and well-structured the summary is. Fidelity measures how accurately the summary captures key points. Redundancy measures repetition or unnecessary phrases.

[0081] The training module 280 allows for fine-tuning or training of the language model 230 on custom datasets highly relevant to the summarization task and domain. While not required, additional training can improve performance by fitting the model to preferred terminology, style, and levels of abstraction for the targeted use case. For example, legal summarization may benefit from a model fine-tuned for legal documents to better handle long, complex content with precise definitions and citations. The training module 280 may be configured to fine-tune the language model 230 using domain-specific datasets. Data preprocessing may include techniques such as formatting, cleaning, and sampling representative content. Various model architectures, such as BERT or GPT-3, can be used for abstract summarization. The model may be trained using maximum likelihood estimation and backpropagation to optimize abstraction performance.

[0082] During operation, a pre-trained or fine-tuned language model 230 is utilized to generate a summary. The training module 280 can be used to customize the model 230 when beneficial; otherwise, a ready-made model may be used directly. For example, a BERT model may be fine-tuned for a large corpus of legal documents to suit the vocabulary, style, and level of abstraction commonly used to summarize legal contracts, and / or GPT-3 may be fine-tuned for technical papers in a specific field such as biomedicine to help summarize long academic documents into concise summaries using common terminology.

[0083] Several training techniques and processes may be used to optimize the large-scale language model (LLM) used for summarization. The model can be pre-trained on a large set of text corpora and then fine-tuned using domain-specific datasets.

[0084] For pre-training, billions of text documents are used to develop general language comprehension. Data sources include web pages, books, academic papers, news articles, and forums. Documents are filtered for diversity to improve the scope of application of topics, styles, and terminology. Cleaning, shuffling, and sampling techniques are used to prepare the pre-training data.

[0085] After pre-training, the LLM can fine-tune a dataset of document-summary pairs within the target domain. For legal summaries, thousands of contracts, case precedents, and submissions with human-written summaries provide examples for specialization. Data cleaning prepares the domain documents.

[0086] During fine-tuning, the model can be trained to generate summaries similar to the examples using maximum likelihood estimation and backpropagation. Hyperparameters such as learning rate, dropout, and epoch can be optimized for the dataset. The training set may be extended using augmentation techniques such as paraphrasing. The optimized model architecture can be selected by evaluating the holdout set.

[0087] Quantitative evaluation metrics may measure key attributes of the generated summaries, such as the following: Compression ratio - the reduction in length from the original text to the summary. A higher ratio indicates more abstraction. Fidelity metrics such as ROUGE, BLEU, and METEOR compare the reference summary with the system output using n-gram overlap. Higher scores indicate better consistency with human-generated reference summaries. Consistency—Readability metrics such as Flesch readability assess grammar, structure, and clarity. Higher scores reflect a more coherent summary. Information density – the ratio of important details such as entities, facts, and figures to substantively smaller words. A higher density indicates that the summary is focused on substantial content. Redundancy analysis flags reused phrases and overlapping semantic content. Lower redundancy improves conciseness. Relevance – Human evaluation or automated classifiers determine how well the summary captures the key information from the source. High relevance improves usefulness.

[0088] These metrics can be logged for each generated summary and tracked over time. These metrics can then be used to guide continuous model optimization and training priorities.

[0089] Many external data sources can provide contextual information to support summaries, namely, the following: Knowledge base: Wikidata, DBpedia, and proprietary knowledge graphs include entities, relationships, and facts to add background information. Academic Resources: Article repositories (arXiv, JSTOR), patent databases, and research catalogs provide scientific context. News & Current Events: News APIs, public datasets, and web scrapers provide timely, real-world context. Financial data: EDGAR filings, company profiles, macroeconomic indicators, and market data provide business context. Public records, such as legal documents, property records, voter information, and census data, contribute to the government context. Product data: Manufacturer databases, e-commerce catalogs, and review sites provide product / service context. User Analysis: CRM systems, sales promotion, web analytics, and other behavioral data are used to tailor summaries to user needs.

[0090] The system can index these sources for efficient lookup, for example, based on entity links, keywords, and semantic searches, to find the most relevant external data for a given summary. Access may be controlled to prevent data misuse. The summarized excerpt may keep the external data concise.

[0091] In exemplary embodiments, summaries can be adapted to different use cases through customized vocabulary, style, length, and formatting. For example: Executives can get a bulleted summary using simple vocabulary. Legal professionals receive summaries using legal terminology that include important citations. The physician uses the explained technical terms to obtain a medically accurate summary of the health record. Engineers obtain technically accurate summaries using properly formatted mathematics / code. Children obtain summaries written using simpler words and at a lower reading comprehension level. A blind user can obtain a text-to-speech summary. Foreign users can obtain summaries translated into their native language. The analysis use cases are optimized in vocabulary and length for easier reading. The report use cases are well-formatted using fonts, colors, and data visualization. Compliance use cases involve concealing or masking sensitive information. A profile containing the user's role, reading level, language, accessibility needs, and purpose guides the customization process.

[0092] In an exemplary embodiment, the abstract is adapted to improve the understanding and usefulness of the target audience.

[0093] Detailed analysis tracks system usage and performance, for example: Request volume, response time, and traffic trends identify capacity needs. Query latency percentiles accurately identify bottlenecks. Memory usage, CPU performance, and storage performance can help optimize the infrastructure. Error rates, failures, and warnings highlight reliability issues. Model prediction load, data API usage, and training iterations guide optimization. Summary evaluation measures quality and satisfaction. The usage patterns analyze common documents, entities, keywords, and queries.

[0094] Analysis can be visualized on a dashboard for monitoring. Anomaly detection triggers alerts. The data guides infrastructure planning and system improvements.

[0095] The described design balances trade-offs such as precision, flexibility, scalability, and transparency.

[0096] Summarization systems can be optimized using robust training methods designed to maximize abstraction accuracy. The large-scale language model at the core of the system can be pre-trained on a massive text corpus to develop general language capabilities. For example, the GPT-3 model can be pre-trained on over one trillion words.

[0097] After initial training, models can be fine-tuned using domain-specific summarization datasets to further specialize them. For example, in the case of legal summaries, a large corpus of legal contracts, case law, and submissions is used alongside human-written summaries. Similarly, for scientific documents, a large dataset of academic papers paired with summaries is leveraged.

[0098] Training data can be prepared through cleaning, formatting, and normalization. The dataset size can be increased using augmentation techniques such as backtranslation and paraphrasing. Hyperparameters can control each training run, including the learning rate, dropout, and number of epochs.

[0099] To evaluate the model after fine-tuning, the system can quantify the semantic similarity to the reference summary using summarization metrics such as ROUGE and METEOR.

[0100] Human evaluation studies can be conducted in which experts, such as lawyers and scientists, assess the summaries for accuracy, fluency, and completeness.

[0101] To benchmark our approach, controlled experiments can be conducted comparing the system against established summarization techniques, such as experiments on legal, academic, and news summarization datasets.

[0102] While some of the system descriptions in this specification focus on text summarization, the system architecture is also designed to support multimedia inputs, including audio, images, and video.

[0103] For audio input such as podcasts, voice messages, and phone calls, automatic speech recognition can be used first to transcribe the audio into text. The resulting text transcript can then be summarized using the system's text summarization capabilities. To maximize transcription accuracy, the system may use an optimized speech-to-text model that is fine-tuned for the target audio domain.

[0104] For image and video inputs, multimedia analysis techniques can be applied to extract key objects, people, scenes, and text segments from the visual content. This extracted visual metadata provides signals for determining importance and relevance when generating a summary. For example, keyframes containing presenters, slides, or illustrations may be prioritized over video. The system's summarization model can be trained to incorporate both text and visual relevance cues.

[0105] After extracting key text and visual elements from multimedia, the system's summarization engine can condense this content (for example, according to the configurable abstraction techniques described herein). The system can generate multimedia summaries optimized for different modalities, such as spoken audio summaries, image storyboards, and video highlight reels.

[0106] Therefore, the system's summarization techniques can be applied beyond text (for example, to rich multimedia use cases). In exemplary embodiments, the same level of abstraction capability can be achieved across images, video, audio, and other types of content.

[0107] Figure 3 is a block diagram illustrating an exemplary method 300 for generating an abstract summary of content items using a Large-Scale Language Model (LLM). In an exemplary embodiment, method 300 may be implemented by one or more of the modules in Figure 2.

[0108] In operation 302, a first content item is identified. The first content item may contain multiple sub-content items, such as sentences and clauses. The first content item can be obtained in various ways, including loading document files, scraping websites, interfacing with content repositories via APIs, recording phone calls, transcribing videos, and extracting text from images. The content item may originally be text, or it may be extracted from sources including audio, video, books, legal documents, medical records, emails, web pages, chats, research papers, news articles, product descriptions, resumes / CVs, police documents, etc. In the case of legal, medical, or other sensitive content, prompt engineering may instruct the LLM not to omit substantial information, regardless of the level of abstraction.

[0109] Optical character recognition (OCR) can be used to extract text from image or video files. Speech recognition can transcribe audio into text. Content items may undergo preprocessing such as formatting, language detection, and entity extraction.

[0110] In operation 304, the abstraction level of the first content item is determined. This determination may be based on user input or API parameters specifying a compression percentage or other abstraction parameters. The target abstraction level is: This can be specified numerically (e.g., 50% compression), using qualitative descriptors (general summaries), or by combining metrics such as word count and the percentage of specific verbs to remove. The user interface, configuration files, or runtime parameters can define the level of abstraction. Presets may exist for different use cases.

[0111] In operation 306, prompts are automatically generated for the LLM. In exemplary embodiments, prompt engineering applies techniques such as sentence reordering, entity substitution, keyword insertion, exemplary output framing, and instructions that guide the LLM to hit an abstraction target. Templates can be defined for each different content type. Prompts can be generated programmatically or using a simple interface.

[0112] For example, proper nouns such as dates, amounts, and product names are: To control the level of abstraction, prompts can be replaced with generic placeholders. Prompts can instruct the LLM to focus on summarizing the content theme rather than reproducing specific details. Furthermore, the order of sentences from the original content can be rearranged within the prompt to improve the context and logical flow of the LLM. Templates can determine the optimal sentence placement based on the relationships between entities, topics, and other semantic elements.

[0113] In operation 308, a prompt is provided to the LLM. The LLM can be implemented using a machine learning framework such as TensorFlow or PyTorch. It can run locally or leverage a cloud API. The summary is generated autonomously after the prompt is submitted. The generated text in the response is extracted programmatically.

[0114] In operation 310, a response is received from the LLM. The response includes a second content item representing the first content item. This representation omits or simplifies sub-content items included in the first content item, based on the level of abstraction.

[0115] In an exemplary embodiment, if the level of abstraction exceeds a threshold, the prompt is enhanced to request one or more inferences and / or one or more external data items. These requests may be used to enrich the representation. External data may provide supplementary information from sources such as knowledge bases, corporate databases, or news APIs to complement the abstracted entities and details. This external data may help maintain consistency and accuracy when actively compressing content. External data may provide supplementary information from sources such as knowledge bases, corporate databases, academic paper repositories, current events, product catalogs, user profiles, and criminal record records, and may be used to add to or complement the abstracted details.

[0116] In operation 312, a representation is applied (for example, used to control output communicated to a target device). Illustrative target devices may include screens, speakers, haptic interfaces, augmented reality displays, etc. The representation can be translated into audio, displayed text, graphics, video, animation, or other formats. In the case of a sales call, this representation can be used to craft conversational responses for an intelligent agent. The responses are contextually appropriate based on the call details. Conversational context elements such as user profiles, conversation history, and business / sales details inform response generation along with the content representation. The system may leverage these contextual cues to create natural and situationally appropriate responses. The representation can be applied to generate visualizations, animations, conversational responses, or other outputs adapted for web pages, reports, emails, briefs, medical summaries, sales calls, books, etc.

[0117] In an exemplary embodiment, iterative prompt engineering guides the LLM to gradually increase the level of abstraction. This process ends when the representation meets metrics such as length, entity density, and lexical complexity compared to the source. Checkpoints can be used to validate the intermediate summary. In an exemplary embodiment, when the level of abstraction is -20%, the prompt may be crafted to require inference with a confidence level of over 80% and / or appropriate external sources. At a -50% zoom, the prompt may be crafted to require relevant external data with a confidence level of over 50% and / or a "looseness" of the corresponding relationship.

[0118] The system can optimize prompt engineering for precise control over the abstraction capabilities of large-scale language models (LLMs) using advanced machine learning techniques. One technical improvement is a prompt engineering model trained to dynamically construct prompts that are adapted to the nuances of the input content and the desired level of abstraction.

[0119] In an exemplary embodiment, the prompt engineering model utilizes a transformer-based neural network architecture, employing an encoder-decoder structure. The encoder captures features extracted from input content items, such as part-of-speech tags, named entities, sentiment scores, topic vectors, and target abstraction levels. The decoder model generates an optimized prompt sequence based on the encoder output.

[0120] During training, the model learns prompt engineering strategies to extract the most accurate summaries from LLMs for each content type and level of abstraction. The trained model can then generalize these strategies to new documents.

[0121] Prompt engineering models can be trained on diverse datasets of {content, prompt, summary} triplets. Content includes text samples exhibiting a wide range of linguistic features and document structures. Prompts may be high-quality, human-written examples at different levels of abstraction. Summaries may be human-generated by domain experts at each target level of abstraction.

[0122] This provides supervised training data for learning the associations between content characteristics, prompt structure, and desired summaries. Human-selected data ensures that prompts achieve their objectives without undesirable biases.

[0123] The model can be trained end-to-end using maximum likelihood estimation. The cross-entropy loss is minimized between the prediction prompt and the human reference prompt. Training continues until convergence and overfitting are reduced by regularization techniques such as dropout.

[0124] To scale and expand the diversity of the training set, data augmentation techniques such as backtranslation, paraphrasing, and noise addition can be used. This improves generalization ability.

[0125] The final trained model can predict the optimal prompt for any new content item and target abstraction level.

[0126] In addition to pre-training, online learning during inference continuously improves prompt engineering. User feedback, such as summary evaluation and editing, can provide personalized training signals. User interactions can be logged as {content, prompts, summaries, feedback} samples.

[0127] Periodically, new samples can be aggregated and used to retrain the prompt engineering model. This allows for adaptation to individual user styles and preferences.

[0128] Active learning selection strategies identify the most beneficial samples for retraining. This maximizes accuracy gains from limited user feedback.

[0129] To avoid impacting users, new prompt engineering models may be evaluated offline first. Only models that pass quality testing may be deployed (e.g., following a blue-green deployment methodology).

[0130] In an exemplary embodiment, multitasking learning helps optimize prompts. Auxiliary tasks, such as predicting the ideal length or level of abstraction of a document, provide useful gradients during training. This can lead to a better shared representation.

[0131] In a multitasking framework, the model shares encoder weights across tasks while maintaining task-specific decoders. The primary task predicts prompt text based on the encoder output. The secondary task predicts characteristic values ​​such as target length.

[0132] By modeling both the prediction from document to prompt and the prediction from document to characteristic values ​​in parallel, the resulting encoder representation becomes specialized for prompt engineering.

[0133] This technique not only mimics human performance but also improves generalization by regularizing the model to capture the attributes necessary for high-quality prompts.

[0134] In addition to supervised learning of human prompts, reinforcement learning (RL) can be used to further enhance prompt engineering.

[0135] In RL, the agent (prompt engineering model) takes action in the environment (LLM) to maximize the reward (summarization accuracy) by generating prompts. The model receives feedback on how well each prompt achieves the target abstraction level. It learns to improve prompts through trial and error.

[0136] The model can be initialized using weights pre-trained with human prompts. RL fine-tunes the prompts to suit the nuances of a particular LLM model. Various policy gradient algorithms, such as REINFORCE, are used for training.

[0137] This allows prompts to be adapted to optimize the abstraction control of individual LLMs. The RL agent learns the subtle differences in how different prompts affect the output of each LLM.

[0138] In interactive settings, collaboration between humans and AI can enhance prompt engineering. Users can provide feedback to generative models, guiding them toward higher-quality prompts.

[0139] Humans possess an intuitive understanding of subtle linguistic concepts that are difficult to capture through fixed rules and training data. Collaborative learning leverages this human capability.

[0140] Active learning queries select the most uncertain or helpful prompts for human feedback, minimizing the number of interactions required.

[0141] The feedback format includes prompt evaluation, prompt selection, prompt editing, and free-form suggestions. This rich feedback will target weaknesses in the model.

[0142] Human collaborators essentially act as additional trainers, providing personalized and contextualized guidance to the prompt engineering model. This tight feedback loop rapidly improves the prompts.

[0143] The trained prompt engineering model can be deployed on a low-latency, scalable, cloud-based architecture as described herein.

[0144] Auto-scaling groups can launch additional model servers based on the load. This maintains fast response times under high demand volumes.

[0145] Request batch processing with dynamic timeouts balances throughput and latency. Caching reduces prompt generation time for common content profiles.

[0146] This architecture integrates with the overall summarization workflow. Prompts are passed to downstream LLM services for post-engineered summary generation.

[0147] This deployment maximizes the availability and scalability of critical prompt engineering capabilities. Reliable prompts determine the overall accuracy of summaries.

[0148] The trained model can employ a variety of techniques to optimize the prompt, including one or more of the following: Content restructuring - Restructures long input text for better context and consistency. Improves LLM understanding. Entity substitution—to control abstraction, less important entities are replaced with placeholders. This allows LLM to focus on essential information. Keyword Insertion - Insert keywords such as "summary" and "paraphrasing" to guide the behavior of LLM. Exemplary output - Provides an exemplary desired output for formatting and establishing speech. Length constraints - Specify a limit on the target word, sentence, or percentage to achieve the abstraction objective. Reading comprehension level - Adjusts vocabulary complexity and sentence structure based on the user's expertise. Makes summaries accessible. Tone Adaptation - Adjusting language style, expertise, and enthusiasm to match user preferences. Transitional words and phrases – Use transition words and phrases to improve the logical flow and consistency in the generated summary.

[0149] The appropriate technologies are selected and combined to build customized prompts for content attributes, abstraction levels, use cases, and user profiles.

[0150] This exponentially expands the space of possible prompts. The learning model is particularly important for identifying the optimal prompt formulation.

[0151] Various metrics may be used to automatically assess prompt quality, including one or more of the following: Abstraction accuracy – Compares the achieved target level with the actual level of abstraction, based on summary length, compression ratio, and information density. Evaluate flow, coherence, and clarity using readability metrics such as consistency-Flesich-Kincaid. Conciseness - Use density metrics to measure conciseness and redundancy. Relevance - Scores how well the prompt generates a summary containing key details from the source content. Fluency - Evaluates the grammar, structure, and naturalness of the generated summary. Variety—quantifies variety and novelty across prompts for the same document using n-gram differences, embedding distances, and other text dissimilarity measures. User evaluation—explicit feedback from users is collected using a 5-point Likert scale that evaluates dimensions such as relevance, consistency, and conciseness.

[0152] These metrics can guide the prioritization of continuous prompt optimization and training. Prompts are selected to maximize the overall metrics.

[0153] This evaluation method ensures high-quality prompts tailored to each document and use case. Accurate prompts determine accurate summaries.

[0154] The system can use neural networks, reinforcement learning, and human collaboration to train a customized model to construct optimized prompts. These prompts dynamically guide the LLM to generate summaries with the desired level of abstraction, length, structure, and style.

[0155] The system can utilize both positive and negative adjustable zoom levels to dynamically generate summaries of source content at various levels of abstraction.

[0156] Positive zoom levels (e.g., ranging from 100% to 0%) can allow for summarization ranging from a complete retelling of the original content to a reduction to only the most essential elements. For example, at 100%, no abstraction is applied, and the entire content is retelling verbatim. As the zoom level decreases towards 0%, less important details are gradually dropped from the summary, while key identities, structures, obligations, and terminology are preserved. At very high levels of abstraction, close to 0%, only the most essential data, such as the content's basic purpose, the parties involved, its duration, and its core components, are retained within the summary.

[0157] Negative zoom levels (e.g., 0% to -100%) allow for expansion of summaries by incorporating additional inferences made about content and / or relevant data from one or more internal or external data sources. For example, at 0% abstraction, it is not possible to add any additional information beyond simply reiterating the original content. However, as the negative zoom level decreases, the system is allowed to make more complex inferences about the source information and include those inferences, such as inferring risks, motivations, or future implications that require deeper analysis. The system may also progressively incorporate more granular external data from various sources to provide context, ranging from general industry averages to specific competitive analyses of the parties involved (e.g., companies or individuals).

[0158] In exemplary embodiments (for example, based on configuration settings regarding the length or size of positive and / or negative responses), positive and negative zoom scales can generate corresponding levels of abstraction, the difference being that negative scale extraction is supplemented with additional inferred insights, contextual data from external sources, and / or metadata such as specificity and confidence assessments.

[0159] Fundamentally, positive and negative zoom abstractions may contain the same summary content about the original data, albeit at varying levels of conciseness. For example, summaries at ±10%, ±20%, and ±50% may all focus on the same key points, statistics, conclusions, etc., from the source material.

[0160] However, while positive scales only provide core summary content, negative scales add value through reinforcement. Thus, these same -10%, -20%, and -50% summaries include not only extracted and condensed content, but also more speculative inferences made by the system, relevant contextual data drawn from external sources, and / or metadata that characterizes additional information.

[0161] For example, a -10% summary might link key figures to relevant industry reports and characterize them as reinforcements with a moderate degree of confidence. A -20% summary might thicken speculative conclusions about motivations, while labeling them as inferences with a low degree of confidence.

[0162] This corresponds to core positive summary content, but enhances it with value-adding augmentation. Including reasoning and confidence metadata further enables users to interpret the value and credibility of the augmentation.

[0163] Therefore, essentially, the positive and negative scales can encompass corresponding levels of abstraction of the core content, but the negative side "zooms out" further by leveraging augmentation and metadata to add value on top of the core summary. This allows for a richer analysis without losing the option to view only the core summary content if needed.

[0164] The system can determine the relative importance of information in the original content using natural language processing techniques such as word frequency analysis, sentiment analysis, and named entity recognition. It uses similarity measures, such as cosine similarity, and heuristics to assess applicability to determine which inferences and external data to include based on relevance. Confidence ratings may be provided along with the added inferences and data to indicate the additional speculative nature or certainty of each. The goal is to balance a concise retelling of the core content with value-added inferences and data to supplement the context, without exceeding reasonable length constraints.

[0165] To achieve this functionality, the system may automatically generate prompts designed to produce a desired level of abstraction from a Large-Scale Language Model (LLM) that generates the summary. At high positive zoom levels, prompts may instruct the LLM to reiterate key elements of the content with specific word constraints, forcing extreme paraphrasing and abstraction. As the zoom level decreases, the word constraints may be gradually relaxed. At a neutral level of 0%, prompts may instruct the LLM to summarize the content as concisely and comprehensively as possible. At negative levels, additional instructions are provided, such as "Speculate on any risks or future implications you can infer from the content, with a confidence rating from 1-10." and "Bring in relevant industry data to provide context, citing your sources and confidence rating."

[0166] The system calculates appropriate word limits, abstraction levels, and instructions to generate prompts across the entire zoom level. It can optimize prompts through iterative testing and feedback. Prompts are designed to control the length, abstraction, reasoning, data integration, and other attributes of the summaries generated by the LLM.

[0167] During training, the system learns how different prompts affect the behavior of the LLM and the quality of the summary at each zoom level. The training process may include one or more of the following: Programmatically generate a wide range of prompts for LLM, with various instructions, abstraction levels, word restrictions, and other attributes. Analyze the output summaries of the LLM to measure attributes such as length, use of inference, incorporation of external data, and conciseness. Summarization quality is scored through metrics such as information coverage, consistency, readability, and human validation. Reinforcement learning is used to associate prompt attributes with summary results and determine the optimal prompt.

[0168] Through sufficient training data, the system learns how to automatically construct prompts tailored to each zoom level that elicit the desired LLM behavior and high-quality summaries with an appropriate balance of abstraction, conciseness, reasoning, data ingestion, and insights.

[0169] Furthermore, in the case of sensitive content such as legal contracts or medical records, the system may identify and store particularly important data, even if it is only a very general summary. The techniques may include one or more of the following: During natural language processing analysis of source content, certain data such as names, dates, identifiers, and requirements are flagged as having high priority for retention. Based on best practices, maintain a list of entities, terms, and data types that are typically required for specific document types, such as contracts or patient records. To retain specific highlighted information within the summary, provide explicit instructions within the prompts generated for the LLM, thereby disabling the abstraction process. To ensure the retention of flagged data, the summaries generated during training are programmatically checked, and feedback is provided to the LLM for any errors. This allows users to tag specific data as essential to maintain before summarizing it. To store different data depending on the document type, we utilize document classification techniques by using conditional logic within prompts. To enable comparison, multiple summary variations are generated, some with sensitive data and some without.

[0170] Through these mechanisms, particularly important data can be identified (e.g., via automation, user input, and / or domain intelligence). The system then ensures its retention, regardless of the zoom abstraction level, through targeted prompt engineering and training. This balances the preservation of information integrity for sensitive content with concise summarization.

[0171] Furthermore, the system can support real-time interaction and zoom level adjustment through user interface control. Some exemplary capabilities may include one or more of the following: Display the original content and summary side-by-side, synchronize scrolling using UI controls such as sliders, dials, or buttons, and change the zoom level on the fly. An animated transition to smoothly show content being added or removed when adjusting the zoom. To switch between a general overview and a more detailed recap, interactively expand or collapse the summary section. A mouseover popup reveals additional inferences or external data when you hover over a portion of the summary. Visual confidence ratings and source citations are displayed directly in the summary interface. A natural language or conversational interface for adjusting the zoom level using voice commands or text. Integration with screen readers, magnifiers, or other accessibility tools to meet the needs of different users. A real-time feedback mechanism that allows users to verify or flag inference issues when reviewing a summary. A summary version history that allows you to switch between previous zoom levels for comparison. User-configured zoom level presets for common abstraction needs.

[0172] These interactive interfaces allow users to dynamically control and customize summaries on demand to meet their real-time needs for reading and understanding complex documents.

[0173] Furthermore, the system can be integrated with external systems via APIs to enable real-time abstraction and analysis of content. Some examples include: A sales automation platform that queries the system to obtain a summary of customer contracts on demand, facilitating transaction discussions. A legal research system that uses summaries to analyze case law and prior art to extract key claims and concepts. A medical record system that retrieves summaries, extracts particularly important information from patient reports, and formats them in a standardized, condensed format for physician review. A financial modeling system that calls an API to summarize long submissions or transcripts of earnings presentations in real time and feeds them into a corporate valuation model. A marketing automation tool that uses summaries to create abridged descriptions of products, services, or other content for campaigns. A recruitment system that uses summaries to create abridged and anonymized resume summaries for screening candidates. An AI assistant or chatbot that uses summarization to condense long-form user queries or requests into a shortened format in order to improve understanding.

[0174] External systems can connect to the summarization API to submit documents, specify zoom levels, receive summaries on demand, and integrate them into downstream processes and applications. Real-time access allows them to leverage summarization capabilities to extract key information, insights, and context, and notify automated decisions and actions.

[0175] The system offers versatile functionality through its real-time zoom level control, external system integration, and use cases that allow users and applications to dynamically customize the summaries of complex documents to suit their specific needs and workflows.

[0176] At negative zoom levels, prompts can be constructed to provide transparency to the rationale behind added inferences and external data. Specifically, prompts can instruct the model to include confidence values, source citations, and explanatory information about why a particular inference or data point was selected.

[0177] For example, the prompt could specify: "Make two inferences about the risks this business deal poses. For each inference, explain your rationale in 25 words or less and rate your confidence from 1 to 10."

[0178] The model then generates the following response: "This deal risks the company overextending itself financially if sales targets are not met (confidence: 7 / 10). Rationale: The expanded production facilities require high fixed costs, so even small revenue shortfalls could be highly detrimental. This deal risks damaging relationships with long-term clients by competing directly against them (confidence: 6 / 10). Rationale: The company's move into this product line will threaten major clients who already sell similar products."

[0179] Similarly, for external data, the prompt may require you to cite the source and explain its relevance, for example: "Bring in two relevant data points from external sources about this company's financial standing. For each data item, identify the source, explain the relevance in 25 words or less, and rate your confidence 1-10."

[0180] The model could respond as follows: "Company's debt increased 30% year-over-year according to their latest quarterly filing (source: SEC 10-Q) (confidence: 9 / 10). This highlights the burden of their recent facility expansions. Company's bond rating was downgraded last month by S&P (source: S&P press release) (confidence: 8 / 10). This signals concerns about the company's financial stability amid rapid growth."

[0181] By extracting explanatory information, the system provides transparency to the rationale and selection process of models for inference and external data integration.

[0182] Furthermore, while this specification often describes a single LLM, the system can leverage multiple LLMs and other trained models to combine their outputs for a more robust summary. Different models have complementary strengths and weaknesses that can be aggregated for improved results.

[0183] A multi-model summary can be compiled using various techniques, including one or more of the following: Multiple candidate summaries are generated by providing the same prompt to different models, and then the ensemble model compares the candidates and selects the best summary using consensus validation. Break down prompts into modular subtasks that best suit different models, such as using legal expert models for contract analysis and financial models for financial data. We iteratively query different models to refine and strengthen the summary, correct errors, and fill in gaps. We use a primary prompt engineering model specifically designed to construct prompts that are suited to the capabilities of each subsummary model. Based on expertise, we utilize different models for different zoom levels, such as leveraging financial models in the negative zoom market data context. To determine the optimal prompt phrasing and model selection for each summarization task, prompt variations are programmatically generated and tested. A consistency checking model is used to scan for conflicting information from different models and perform error reconciliation. The confidence ratings are compared across models, and higher confidence responses are given preferential weighting.

[0184] By combining multiple diverse models, the system can leverage complementary strengths in language understanding, reasoning ability, data integration, and domain-specific knowledge. Orchestration mechanisms help select, refine, and aggregate model outputs to generate higher-quality summaries compared to relying on a single model alone.

[0185] Advanced prompt engineering techniques combined with multi-model orchestration provide transparency to the summarization process while improving output quality, context, and user confidence.

[0186] This system allows for flexible configuration of the amount of flexibility or margin in the generated summary length for both positive and negative zoom levels.

[0187] In the case of positive zoom levels, where summaries are abbreviated by increasing the level of abstraction, the degree of length reduction can be customized based on user needs. This system provides settings to control the rate of length reduction as the abstraction percentage increases.

[0188] For example, by default, the word count is reduced by 15% for every 10% increase in the level of abstraction. However, users can adjust this compression rate; for instance, to reduce the word count by only 10% for every 10% increase in abstraction, for a more gradual summarization.

[0189] Regardless of the exact compression ratio, this system ensures that the most important information is always included, even in a significantly abridged summary. This is achieved, as previously described, by automatically identifying key sentences and important points using extractive summarization techniques.

[0190] In the case of negative zoom levels, which extend summaries by eliciting inferences, integrating external data, and providing transparency to the system's rationale, the degree of leeway in length augmentation can also be configured.

[0191] By default, the system may allow the summary length for a given negative zoom level to be up to twice the summary length for the corresponding positive zoom level.

[0192] For example, at 90% abstraction, a summary might be 100 words long. At -10% abstraction, the default configuration allows for summaries of up to 200 words, enabling significant expansion with inference and external data.

[0193] However, users can customize the margin level so that longer summaries are possible at negative zoom levels if desired. The system provides a setting to control the maximum expansion factor so that the length of a positive summary can be up to five times that of a given negative zoom percentage.

[0194] Therefore, in the example above, the -10% summary can be up to 500 words, allowing for great flexibility in incorporating additional information.

[0195] Regardless of the configuration, this system ensures a concise and consistent summary by controlling the redundancy and repetition of added inferences, data points, and transparency details. Prompts are designed to elicit focused supplementary information relevant to the user's needs.

[0196] The modular design of this system allows for adjustment of the degree of configurability for both positive and negative zoom levels without affecting other functions. Customizable compression and expansion factors give the user precise control over the length of summaries at all levels of abstraction.

[0197] Combined with the previously described capabilities for multi-model orchestration, advanced prompt engineering, and dynamic data integration, this configurability enables the generation of summaries with the appropriate level of detail, context, and transparency for each user's specific use case.

[0198] For example, analysts may prefer a significantly condensed overview at positive zoom levels and expanded detail at negative zoom levels to understand reasoning and sources. Lawyers may prefer a more balanced length across zoom levels to maintain equilibrium. Executives may prefer minimal variation in length across the entire zoom range to maintain messaging consistency.

[0199] By offering flexible settings for both compression and expansion factors, this system can adapt to these diverse needs and optimize information presentation for different roles and tasks. This customizability gives users complete control over balancing conciseness, completeness, and transparency in the generated summaries.

[0200] Exemplary Workflow

[0201] Multistage workflows, such as the exemplary workflow below, can be used to support the system's functionality through one or more of the following: robust ingestion, content analysis, abstraction targeting, tailored summarization, and refinement. The flexible architecture allows for interchangeable components while maintaining a focus on customizable abstraction capabilities.

[0202] Request Retrieval

[0203] The summarization workflow can begin when a client request is received by the system's API gateway. Robust ingestion prepares the request for processing.

[0204] load balancing

[0205] Incoming requests may be distributed across the entire API server to handle high traffic volumes. The approach may include one or more of the following: DNS routing to distribute requests based on geography, usage patterns, and current load. A reverse proxy server, such as Nginx, that forwards requests to available upstream APIs. A Kubernetes-like container orchestrator that distributes load across dynamic pods. An auto-scaling group launches additional instances under heavy load. A caching layer like Varnish that directly handles recurring requests.

[0206] Load balancing provides scalability and availability during traffic spikes.

[0207] certification

[0208] A request may be authenticated to verify the caller's identity and authorization, and such authentication may include one or more of the following: API keys, OAuth tokens, and other credentials are supported. The middleware extracts and validates credentials from the header or parameters. Credentials are checked against access control lists and usage quotas. Role-based access restricts functionality based on user permissions.

[0209] Proper authentication ensures that users can only access authorized features.

[0210] verification

[0211] Before processing, the request may be validated against the expected format using, for example, one or more of the following: The payload is checked against JSON schemas, XML DTDs, and other declarative constraints. Size limits, text length, and other constraints are enforced. Parameter values ​​are sanitized to prevent injection attacks. Usage quotas and rate limits prevent misuse and ensure fair use.

[0212] Verification protects downstream services from unexpected inputs.

[0213] cueing

[0214] Validated requests are queued asynchronously for robustness, which may include using one or more of the following, for example: Message queues such as Kafka, RabbitMQ, or SQS buffer the requests. Back pressure prevents overloading of downstream services. Batch processing optimizes throughput and overhead. Prioritization ensures that VIP requests are processed quickly.

[0215] Reliable queuing smooths out traffic spikes for efficient processing.

[0216] instrumentation

[0217] Detailed metrics may be collected regarding the request, which may include, for example, one or more of the following: API calls are metered for billing, analysis, and monitoring purposes. Current usage will be checked against quotas and limits. The audit log records all requests, including their parameters and response codes. Tracing allows requests to be linked across microservices using request IDs.

[0218] Instrumentation enables usage-based billing, planning, and troubleshooting.

[0219] The purpose of ingestion may be to maximize the security, reliability, and / or monitoring of requests before summarization.

[0220] Content Analysis

[0221] Before summarizing, the content may be analyzed to determine the optimal approach, which may include, for example, the following:

[0222] Metadata Extraction

[0223] Metadata can be extracted to identify the type and characteristics of the content, specifically as follows: The header, filename, MIME type, etc., can indicate the format of the file, such as text, image, or video. Media attributes such as resolution, duration, and size can provide insights. Hashing can detect duplicate content. Structural analysis can identify logical sections and layouts. Optical character recognition (OCR) can extract text.

[0224] Metadata can provide a profile of content for downstream decision-making.

[0225] Natural Language Processing

[0226] In the case of text, natural language processing extracts semantic features, which include, for example, one or more of the following: Parts of speech, entities, emotions, technical terms. Linking concepts through relationships using a knowledge graph. Discovering broad themes through topic modeling. Identifying the purpose of information through question-and-answer sessions.

[0227] Text analysis provides the semantic understanding necessary for abstraction.

[0228] User preferences

[0229] For example, submitted user preferences guide customization, which may include one or more of the following: Summary length, style, and other formatting options. Adjusting vocabulary to match the level of expertise. Displaying information for purposes such as overview versus details. Accessibility needs for text alternatives

[0230] User preferences can directly inform the desired summarization behavior.

[0231] Thorough content analysis provides the necessary context to tailor the summarization approach.

[0232] Abstraction target setting

[0233] The optimal level of abstraction can be dynamically determined for each summary request, for example, based on the following:

[0234] Content Factors

[0235] The following attributes of the content itself can guide the target setting for abstraction. These are as follows: Highly technical or complex content requires more abstraction in order to simplify it. Long content can be actively condensed through abstraction. Unfocused content can benefit from abstraction, which helps to extract important ideas. Clear and concise content sometimes requires little abstraction.

[0236] Media types such as audio and video may prefer a different abstraction approach than text.

[0237] The complexity and characteristics of the content significantly influence the need for abstraction.

[0238] User parameters

[0239] User preferences and / or requirement parameters can be further refined, including, for example, one or more of the following: The desired length of a shorter summary that requires a higher level of abstraction. Specifying the purpose of the overview to prioritize greater abstraction. Modifications are possible that suggest a higher level of abstraction is acceptable. Resource constraints, such as those on mobile devices, that limit the abstraction techniques that can be executed. A direct requirement for the depth or breadth of abstraction.

[0240] User parameters can provide clear guidance regarding the desired level of abstraction.

[0241] Use case

[0242] Exemplary use cases that highlight the usefulness of the disclosed technique may include, for example, one or more of the following. For lawyers, summarize long legal contracts into concise overviews. For students and researchers, generate abstracts of academic papers. Extract and condense medical records into reduced summaries for review by physicians. Provide summaries of news articles for general readers. Other exemplary use cases as described in the present specification.

[0243] General use cases can be used to identify established best practices for abstraction in specific contexts, including, for example, one or more of the following. Literature reviews for research require highlighting the key contributions of papers. Summaries of legal contracts focus on core obligations and exclusions. Social media trends prioritize broad overviews over details. Summaries of medical records emphasize diagnostic details over patient background.

[0244] A use case may have a known abstraction profile.

[0245] Policy Engine

[0246] The policy engine can encode the expertise of advisors into automatic judgments, including, for example, one or more of the following. Rules map content factors, user parameters, and use cases to an ideal abstraction level. Policies incorporate best practices from advisors and governance committees. Default settings reasonably handle ambiguous cases. Context-aware judgments maximize the overall utility for each request.

[0247] The policy engine scales expertise for large-scale personalization.

[0248] To enable optimally customized summaries, precise dynamic abstraction targeting may be used.

[0249] summary

[0250] A specialized summarization service generates abstract summaries tailored to the target level, which may include, for example:

[0251] extraction

[0252] The extraction method identifies key sections in the original content for summarization, namely one or more of the following: Statistics such as word frequency-inverse document frequency (TF-IDF) are used to score sentences. Graph algorithms model the relationships and centrality of sentences. A supervised classifier predicts the relevance of sentences based on annotations. Constraints enforce limitations on diversity, comprehensiveness, and length.

[0253] Extraction involves extracting and condensing source content into a concise summary.

[0254] abstraction

[0255] The abstract method generates a new text that captures the core semantic content, namely, one or more of the following: The encoder-decoder neural network fuses content into a vector representation. The attention mechanism focuses on decoding important information. The beam search generates multiple candidate summaries for selection. Paraphrasing condenses and refines text.

[0256] Abstractive abstraction generates fluent natural language summaries.

[0257] Query-based

[0258] The query-based method adapts the summary to the user's information needs, that is, in one or more of the following ways. A query analyzes the purpose of information. A knowledge base provides relevant facts. Using extraction and abstraction, a fact-centric summary is generated.

[0259] Query-based summarization delivers focused, highly relevant information.

[0260] Hybrid

[0261] The hybrid approach combines extraction, abstraction, and querying, that is, in one or more of the following ways. Narratives from multiple documents merge common information. An encoder incorporates facts from the knowledge base. User feedback iteratively adapts the summary.

[0262] The hybrid approach maximizes refinement through synthesis.

[0263] Specialization improves relevance for specific use cases, while modularization enables diverse configurations.

[0264] Abstraction control

[0265] The completed summary is adjusted to a target abstraction level, that is, as follows.

[0266] Compression

[0267] A long summary is compressed to focus on important ideas, that is, in one or more of the following ways. Scoring separates core ideas from peripheral details. Similar sentences are consolidated through paraphrasing. Removing redundancy makes content more efficient.

[0268] Compression adapts a redundant summary to a desired length.

[0269] expansion

[0270] A sparse summary is expanded to add context that provides information, namely one or more of the following: Facts are searched and retrieved from the knowledge base to enrich the content. Descriptive details are incorporated from the source material. Analogies and examples illustrate concepts.

[0271] The extension incorporates color and improves clarity.

[0272] refined

[0273] The iterative refinement process aligns the abstraction with the target, namely, one or more of the following: Automated techniques adjust vocabulary, length, and structure. Manual review by editors provides human guidance. User feedback trains the model and suggests edits. Multiple refinement cycles converge towards the objective.

[0274] Refinement involves precisely adjusting abstraction to meet specific needs.

[0275] evaluation

[0276] Automated metrics quantify the level of abstraction, that is, one or more of the following: Text complexity analysis determines vocabulary and readability. The semantic equivalence metric compares semantic preservation. Expert review assesses the appropriate width and depth.

[0277] The evaluation verifies the accuracy and quality of the abstraction.

[0278] The essence of this technological improvement lies in precise abstraction control tailored to each requirement.

[0279] delivery

[0280] The user will ultimately receive a customized summary, which will be one or more of the following: The metrics used provide information for monitoring, billing, and optimization. For performance reasons, output can be cached globally or per user. To facilitate improvement, users provide feedback through ratings and comments. The summary is formatted for seamless integration into the client's workflow.

[0281] Smooth delivery completes the summarization workflow.

[0282] Figures 4A to 4D are a series of block diagrams depicting exemplary content, including chat, which is represented at multiple zoom levels in the user interface in response to user input. For example, Figure 4A is an exemplary representation of chat at the first zoom level (e.g., 100%), Figure 4B is an exemplary representation of chat at the second zoom level (e.g., 50%), Figure 4C is an exemplary representation of chat at the third zoom level (e.g., 25%), and Figure 4D is an exemplary representation of chat at the fourth zoom level (e.g., -10%).

[0283] Exemplary User Interface

[0284] The system features an intuitive user interface that allows anyone to effortlessly summarize content in real time at any level of detail. Highly intuitive slider-based controls decouple the complexity of the latest AI technology, making dynamic summaries accessible.

[0285] The main interface displays a zoom slider ranging from -100 to +100. This full-spectrum slider allows for seamless reduction of the overview or expansion of details. Additional controls provide users with precise, real-time customization.

[0286] Zoom lock locks the level in place as the user scrolls, ensuring a consistent viewpoint for the summary. The zoom speed slider controls the rate at which the abstraction changes. Faster zoom compresses the content quickly, while slower zoom provides a smoother, more gradual transition.

[0287] The zoom window sets the visibility of abstractions around a locked level, for example, by showing ±10% to maintain context stability. The extractive / abstract slider biases the technique, with extractive prioritizing important sentences and abstract prioritizing paraphrases.

[0288] In negative zoom mode, the inference dial controls the depth of inference, the external data knob balances internal and external content, and the transparency slider reveals the reasoning. Users can also directly ask questions to trigger Q&A extensions.

[0289] By clicking on any section of the summary, users can explore its origins. For example, clicking on "Inference" reveals the reasoning chain and the data that supports it. Clicking on "External Data" reveals the integrated sources. Users verify that the supplemented information is relevant and appropriately attributable.

[0290] For transparency, the system provides metadata such as confidence scores for inferences, relevant citations for facts, timestamps for time-constrained data, and algorithms used to generate summaries. Users can provide feedback on these aspects.

[0291] The system architecture combines various AI technologies to enable real-time performance and personalization. Specifically, it is as follows:

[0292] Extractive and abstractive summarizers use encoder-decoder networks trained on domain-specific corpora to identify, extract, and condense key ideas. Classifiers use convolutional neural networks to categorize content facets. Question answerers use transformer architectures to retrieve and extract key details.

[0293] Large-scale language models generate fluent summaries in a target style with appropriate grammar and consistency. The inference engine extrapolates insights through neural symbolic reasoning and knowledge graph traversal. The data retriever integrates external sources using dense retrievers and inverse indexes.

[0294] Sentiment analyzers, readability predictors, and user feedback engines further enhance personalization. An orchestrator manages model workflows based on zoom settings and usage context. The distributed microservices design scales on demand.

[0295] Real-time caching indexes pre-calculated intermediate summaries, inferences, and data for low-latency assembly to complete summaries. An optimized data store sustains petabytes of training corpora, documents, and public / private data sources.

[0296] This architecture supports summarizing diverse content types to meet the needs of any given user, specifically as follows:

[0297] Legal documents such as contracts and briefs are summarized for the benefit of lawyers who will review the details or those who request executive reports. Technical specifications are abstracted for each engineer according to their level of expertise.

[0298] Physicians summarize medical research and patient health records based on their expertise. Financial analysts extract and condense revenue reports, financial literature, and market data at the desired zoom depth. Policy advisors summarize rules and recommendations for stakeholders.

[0299] Students summarize textbooks, papers, lectures, and online materials at an appropriate reading level. Journalists abstract interviews, scientific research, and background sources into news articles for a general audience.

[0300] Experts summarize research papers and field-specific documents and share them with colleagues. Government analysts summarize reports across sources to identify patterns and derive insights.

[0301] The system adapts the vocabulary, prose style, and background knowledge within the generated summaries to each user's level of expertise, technical proficiency, and educational proficiency.

[0302] In enterprises, interfaces can be integrated into productivity tools, knowledge management systems, and collaboration workflows to enhance document understanding and sharing. Summarization helps workers efficiently compile dense information.

[0303] Seamless zoom and transparency empower users to dynamically control how much information is shared. Sensitive details can be zoomed out, while inferences can be zoomed in for analysis. The system improves productivity across diverse use cases.

[0304] The highly intuitive interface makes the system accessible to anyone who needs to summarize or understand complex documents in real time. Smooth zooming, precise control, personalization, and transparency allow users to dynamically adapt the perspective, depth, and detail of the summary to their precise needs.

[0305] The system may include one or more user interfaces that allow for easy configuration and access to multi-resolution zoomed abstractions at both positive and negative scales.

[0306] Specify the zoom level

[0307] Users can specify custom zoom levels in terms of positive and negative scales using slider controls, text entry fields, or preset buttons, as follows:

[0308] Zoom level slider

[0309] This allows users to specify the desired level of abstraction for a given summarization task. Common presets include 10%, 25%, 50%, and 75% in both directions.

[0310] Access to positive summaries

[0311] The positive zoom interface displays the summary generated for the specified conciseness level, namely, the following:

[0312] Positive summary view

[0313] At a glance, users can see the core extracted and condensed content of the original source material at various levels of abstraction, from a general overview to more detailed information.

[0314] Excerpts of the summaries are displayed directly on the interface to allow for quick scanning. The complete summaries can be expanded or downloaded for further analysis.

[0315] Enhanced negative summary

[0316] The negative zoom interface corresponds to positive summary content, but it can be extended with additional enhancements, namely:

[0317] Negative summary view

[0318] While the same core summary content exists, this version is supplemented with inferred insights, contextual data from external sources, and metadata reservations.

[0319] The inferred conclusions are labeled with confidence scores. Relevant data links are characterized by their relevance. The specificity of the reasoning for inclusion is displayed.

[0320] This allows users to quickly view core summary content while leveraging additional features to add value, if desired.

[0321] Comparison of positive and negative pairs

[0322] A split-screen view is available to compare positive and negative summaries side-by-side. Specifically, it works as follows:

[0323] Split screen comparison

[0324] This allows users to directly compare the core positive summary with the augmented negative version. The augmentations are clearly indicated, while still allowing easy reference back to the original core summary content.

[0325] Interactive metadata

[0326] The reservations regarding negative summary augmentation metadata are interactive, specifically as follows:

[0327] Interactive metadata

[0328] Mouse operations on the confidence score reveal the underlying reasoning and logic behind the inference. Clicking on the relevance assessment displays key extracts from linked external sources. Specificity can be expanded to show the complete trace of the evidence that led to its inclusion.

[0329] This allows users to transparently assess the reliability and value of reinforcements as needed.

[0330] Customizable reinforcement display

[0331] Options are provided to filter and concentrate reinforcements, namely as follows:

[0332] Enhancement display settings

[0333] Users can choose to view only augmentation that exceeds a certain confidence threshold, or only augmentation linked to a specific domain of an external source. Specific and relevant metadata can be toggled on or off.

[0334] This allows for customized control over negative summarization, focusing on the most relevant reinforcement.

[0335] Retaining the underlying source data

[0336] The original source document is always accessible, as follows:

[0337] Access to source data

[0338] Links are provided that allow you to delve into specific sections of the source data that contributed to the given summary excerpt. This makes it possible to trace back to the underlying evidence.

[0339] The source material is also available independently of the summary for direct access when needed.

[0340] The zoom level in the user interface can be configured to correspond to a 100% to an abstraction level.

[0341] For example, as follows: +100% zoom = 0% abstraction +50% zoom = 50% abstraction 0% zoom = 100% abstraction

[0342] On the negative side, the following applies: -100% zoom = 0% abstraction -50% zoom = 50% abstraction 0% zoom = 100% abstraction

[0343] Therefore, in this configuration, the zoom level decreases as the level of abstraction increases in both directions.

[0344] User Interface Control

[0345] The zoom level slider control reflects this relationship, specifically as follows:

[0346] Zoom slider

[0347] The rightmost position, +100%, represents no abstraction, showing the full content.

[0348] As the slider moves to the left, the zoom level decreases and the level of abstraction increases.

[0349] At the midpoint of 0%, the content becomes completely abstract from its essence.

[0350] In the negative domain, the enhanced summary is progressively further abstracted.

[0351] The slider allows for smooth adjustment of the zoom / abstraction level. Text entries and presets can also be used.

[0352] Corresponding positive and negative summaries

[0353] In this configuration, the positive and negative summarization levels correspond to each other. That is, as follows: +80% zoom represents the same level of summarization as -80% zoom, +60% corresponds to -60%, +40% corresponds to -40%, and so on.

[0354] The difference lies in the negative aspect of adding supplementary information and metadata on top of the core summary content.

[0355] Therefore, +50% and -50% may contain the same important extracts and condensed points from the original data. However, -50% is reinforced by inferences, external data, and reservations.

[0356] User interface examples

[0357] The user interface reflects this corresponding relationship, as follows: Positive summary Negative summary

[0358] Despite being reversed, the +80% and -80% summaries contain the same core content. The negative version simply reinforces it using augmentation.

[0359] Advantages of zooming versus abstracting

[0360] Representing zoom as the inverse of abstraction has several advantages, namely, the following:

[0361] As abstraction increases, it may become more intuitive for users to think in terms of "zooming out."

[0362] The metaphor of zooming in for more detail and zooming out for a broader overview is familiar.

[0363] It is logical to have 100% as a starting point to which abstraction is not applied.

[0364] The bidirectional zoom slider visually represents a continuum of abstraction levels.

[0365] Therefore, mathematically, zooming is the inverse of abstraction, but conceptually, this relationship aligns well with the capabilities of a system. Interfaces can effectively utilize the zoom metaphor.

[0366] Implementation details

[0367] There are several important details regarding the implementation of this relationship in the summarization system, namely, the following:

[0368] Configuration settings

[0369] The system allows you to configure whether zooming is treated as the inverse of abstraction or as an independent level. This provides flexibility.

[0370] Calculation logic

[0371] The abstraction algorithm utilizes the configured zoom / abstraction level during processing. The conversion between the two is trivial.

[0372] Interface Linkage

[0373] The interface links the zoom slider position / text entry to properly activate the abstraction engine. The displayed summary is dynamically updated.

[0374] Server-side processing

[0375] The interface can display zoom control, but the abstraction can be handled on the server side. This avoids the client device needing to handle the entire processing logic.

[0376] The equivalence of zooming and abstraction

[0377] The system remembers the matched zoom level and abstraction level to enable linking the interface with the summary results.

[0378] User preferences

[0379] Users can override the default settings if they prefer to treat zoom and abstraction as independent concepts rather than inversely related. This setting will be permanently retained in the user profile.

[0380] Use case

[0381] Here are some examples of how users can leverage this relationship between zoom and abstraction:

[0382] research analysis

[0383] Researchers need to summarize a collection of medical journals to extract key insights. Using a zoom slider, they interactively adjust to the desired level of abstraction, as follows:

[0384] At +100% zoom, no abstraction is applied, and the full journal content is displayed.

[0385] Slide left to zoom in by +50% to get a reasonably concise summary of the core findings for each journal.

[0386] Sliding further to the left, up to +20% zoom, provides a more concise summary that focuses on key statistics and conclusions.

[0387] At 0%, the summary will consist of five highly condensed sentences.

[0388] In the negative domain, a -20% summary involves adding speculative extrapolation and external data to the core content.

[0389] Contract Review

[0390] Lawyers need to analyze long contracts, leveraging the relationship between zoom and abstraction. Specifically, as follows: +100% zoom shows the entire contract. +80% provides a good overview of the contract structure and terminology. +50% extracts and condenses sections into key clauses and obligations. 0% gives a very general summary of the purpose and parties. -50% zoom reinforces the same key clauses with inferred risks and precedents. -80% zoom further reinforces the overview with insights into negotiation considerations.

[0391] Literature synthesis

[0392] Students need to synthesize key points from numerous research papers related to their dissertation. They should use a zoom interface, specifically: +100% zoom shows the complete papers; +75% zoom provides a condensed summary of the findings in each paper; +50% zoom further focuses the summary on core contributions; 0% zoom gives a very short summary of one or two sentences; -50% zoom enhances with speculative links between papers; -75% zoom extracts broader themes and trends and reinforces them across the literature.

[0393] Users can fluidly adjust the zoom level to control abstraction in both directions and leverage negative augmentation. The system handles translating zoom to abstraction levels during processing.

[0394] Exemplary Use Cases

[0395] Here are some examples of how users can leverage these interfaces when interacting with the system:

[0396] Contract Analysis

[0397] The lawyer needs to quickly analyze the 100-page contract before the next negotiation. Using a system, they will generate positive and negative zoomed summaries, which are as follows:

[0398] To obtain an overview and finer details, specify zoom levels of 25%, 10%, and 5% in both directions.

[0399] Scanning 25% positive summaries provides a general understanding of the contract's structure and terminology.

[0400] Focusing on 10% of negative summaries in specific sections, we examine key points enhanced with domain expertise regarding inferred risks, precedents, and negotiation considerations.

[0401] By comparing 5% positive and negative summaries side-by-side, it becomes possible to meticulously analyze the details of the rules while still benefiting from the reinforcement.

[0402] Interactive metadata allows for the assessment of the confidence and relevance of augmentation, enabling the determination of its effectiveness.

[0403] All summaries are linked to the text of the underlying contract for verification as needed.

[0404] Literature review

[0405] Graduate students need to research literature on topics related to their master's thesis. The system is used to quickly process a large volume of academic papers, specifically as follows:

[0406] A broad 75% positive abstract provides a general overview of the paper's content, findings, and conclusions.

[0407] A more granular 25% negative summary also incorporates inference-based extrapolation of the student's insights into their own master's thesis topic.

[0408] Students filter reinforcements to show only those inferred associations that exceed a 60% confidence threshold.

[0409] Scan negative summaries to obtain outlines of core papers, along with linkages likely to be relevant to your work.

[0410] The underlying papers are readily accessible from all abstracts for further examination as needed.

[0411] Proficiency in the codebase

[0412] New software engineers need to quickly catch up with a large and complex codebase. They need to use the system to understand their situation at multiple levels, namely:

[0413] A 50% positive summary provides an overview of the architecture of key components and features.

[0414] The 10% negative summaries describe individual modules and classes, reinforced with inferred dependencies and called integration points.

[0415] Comparing them side-by-side provides both an enhanced overview and a more granular summary, highlighting the insights gained.

[0416] By hovering over specificity metadata, the evidence chain reveals how specific inferences were made from the code.

[0417] The source code will be linked from all summaries for practical orientation.

[0418] In these examples, users can leverage corresponding positive and negative summaries at various levels of abstraction to efficiently analyze large source materials supplemented with AI-generated insights. The interactive interface allows users to consume, verify, and benefit from core extracted and condensed content and value-added supplements.

[0419] Exemplary APIs and SDKs

[0420] To enable seamless integration with third-party applications and workflows, the system exposes its real-time dynamic summarization capabilities through robust APIs and SDKs.

[0421] These programmatic interfaces enable intelligent agents, customer service systems, research tools, and other applications to leverage the system's advanced AI summarization and transparent features on demand.

[0422] The core API is the Summarize API, which accepts content items and the desired zoom level as input parameters. API requests specify the content to be summarized via either text input or a URL, specifically as follows:

number

[0423] The API response includes a summary that has been adapted to the requested zoom level and generated accordingly, namely:

number

[0424] Additional parameters can be used to specify the user or use case context to personalize the summary, control its length and style, set caching options, and specify other configuration details.

[0425] To search and retrieve pre-calculated and cached summaries, the Get Summary API allows you to directly search and retrieve indexed summaries by specifying the content ID and zoom level.

number

[0426] This enables ultra-low latency summarization using cached intermediate results, compared to dynamically generating new summaries.

[0427] To programmatically zoom in and out of summaries, the Zoom Summary API allows for incremental adjustment of the level of abstraction without resubmitting the complete content. Specifically, it works as follows:

number

[0428] A summary updated with the new zoom level is returned. This supports exploratory workflows that delve into details.

[0429] For transparency, the Explain API provides attribute data for any section of the generated summary, namely, the following:

number

[0430] This returns metadata such as inference probabilities, source citations, the algorithms used, and other transparency information regarding their summarized excerpts.

[0431] The Query API allows you to programmatically ask questions to broaden summary coverage of important topics, specifically as follows:

number

[0432] Returns a concise answer excerpt that can be inserted into the summary.

[0433] To programmatically integrate external data, the Augment API allows you to pass content for summarization along with data sources, search queries, or other informational needs. An augmented summary incorporating the external data is returned.

[0434] For training and optimization, the system provides APIs for searching and retrieving usage analytics, submitting user feedback, managing document collections, uploading ontologs, and configuring pipelines. The microservices architecture allows each API component to scale independently.

[0435] The SDK makes it easy to integrate these APIs into a variety of applications, specifically as follows:

[0436] Summarization SDK - A core wrapper for generating, zooming, and explaining summaries.

[0437] Search SDK - Summarizes search results and documents returned from queries.

[0438] Customer Service SDK - Summarize customer case studies and CRM data to resolve issues more quickly.

[0439] Agent SDK - Enables bots and assistants to summarize conversations and documents.

[0440] Analytics SDK - Summarizes reports, dashboards, and data visualizations.

[0441] Research SDK - Extracts and condenses academic papers, articles, and citations for researchers.

[0442] The API and SDK enable any application to augment existing workflows with dynamic summarization capabilities. Examples include:

[0443] Intelligent agents for customer service can summarize lengthy complaints submitted by users and quickly understand the core issues and history before responding.

[0444] Legal discovery applications can extract and condense key facts and evidence from large document collections, allowing for on-demand digging into details.

[0445] Market intelligence tools can summarize the latest news, social media, and reports to detect changes in trends and sentiment.

[0446] A code documentation system can generate a concise and descriptive summary of a source code module at a level desired by the programmer.

[0447] A research paper recommendation engine can summarize a collection of papers on topics of interest to scientists, bringing to light relevant and new findings.

[0448] By providing programmatic access to its summarization capabilities, the system enables the integration of dynamic, personalized summaries into an endless array of use cases across industries and verticals. Powerful APIs and SDKs make the technology accessible to improve workflows in any domain.

[0449] Exemplary management and control and user interface

[0450] User Management

[0451] The system allows for the creation and management of user accounts using role-based access control. Single sign-on integration with corporate directories is supported.

[0452] The user management module allows administrators to do the following:

[0453] Create user profiles for employees, customers, or external users. You can specify user attributes such as name, email, department, and location.

[0454] Assign roles such as viewer, editor, reviewer, and administrator. Each role is mapped to predefined permissions, such as content summarization, summary editing, and system configuration.

[0455] Import users and groups from a corporate directory like Active Directory via LDAP synchronization. This enables single sign-on based on corporate credentials.

[0456] Manage passwords, reset forgotten passwords, and set password policies and strength requirements.

[0457] Multi-factor authentication is enabled through options such as biometrics, security keys, and one-time codes.

[0458] Create user groups for collaboration, such as teams, departments, and internal / external users. Share summary settings and permissions between groups.

[0459] View activity logs to audit user actions such as summarized documents, queries performed, and changed settings. Filter logs by date range, user, content type, and more.

[0460] For example, a media company could deploy separate user groups for writers, editors, fact checkers, reviewers, and administrators. Writers could summarize content, editors could edit summaries by locking the zoom level, and reviewers could provide feedback on the quality of the summaries.

[0461] Model Management

[0462] Administrators can configure the AI ​​models used for summarization based on their accuracy, performance, and cost needs. This system utilizes a modular ensemble of models.

[0463] The model management module allows administrators to do the following:

[0464] Select extractive and abstraction models from a model catalog that includes options such as BERT, GPT-3, T5, BART, and other encoder-decoder networks fine-tuned for summarization.

[0465] To query summaries, select question-answering and dialogue models such as CoQA, QuAC, and DiaLoGT. Choose a model optimized for conversational or factual responses.

[0466] Add custom large-scale language models such as GPT-3, Codex, and PaLM. Fine-tune them using domain-specific texts to fit vocabulary and style.

[0467] Set hyperparameters for the model, such as length, temperature, and top k samples. Higher temperatures result in more creative but blurry summaries.

[0468] Create an ensemble that combines extractive models, abstractive models, and question-answering models. While the ensemble improves accuracy, it may increase latency.

[0469] Manage model versions, evaluate new models in an offline staging environment, and then approve them for production.

[0470] The system automatically switches models based on criteria such as user role, content type, and language. For example, a simpler model is used for users with low expertise.

[0471] For example, a healthcare company could use an abstract model trained on medical texts to generate summaries using appropriate terminology. A question-answering model could provide a useful explanation of a clinical concept.

[0472] Summary Structure

[0473] Administrators can define summarization policies based on user needs, ensuring compliance with company standards regarding length, style, formatting, and reading comprehension level.

[0474] The summary configuration module allows administrators to do the following:

[0475] Set length limits for different zoom levels, such as 250 words for 85% zoom and 500 words for 70% zoom. Enforce a minimum length for overly concise models.

[0476] Configure target lengths for different user roles and content types. Executives may receive a 100-word preview, while analysts may receive a 500-word summary.

[0477] The vocabulary, prose style, and reading comprehension level of the summary are customized based on the user's education and technical abilities.

[0478] We use mature content filtering and human review workflows to block offensive, inappropriate, or unprofessional language.

[0479] Choose output formats such as simple text, rich text, HTML, PDF, and audio. Supports accessibility-friendly formats.

[0480] This process involves concealing or masking sensitive data that conforms to defined patterns, such as credit card numbers, phone numbers, and addresses.

[0481] To prevent the leakage of confidential, proprietary, or trade secret information.

[0482] A classifier model is used to label the generated summaries based on credibility, tone, and other attributes.

[0483] For example, an internal social network can be configured to use simple vocabulary that is suitable for the mainstream audience when summarizing posts and conversations.

[0484] Performance optimization

[0485] Administrators can optimize system performance based on usage patterns for low-latency summarization and efficient resource utilization.

[0486] The performance optimization module provides the following:

[0487] It caches pre-calculated extractive summaries, Q&A responses, and external data for common content types and zoom levels.

[0488] Index content, intermediate output, and data sources for ultra-fast lookup, search, and retrieval. Optimize the frequency of index rebuilding.

[0489] To parallelize requests and reduce latency, scale model inference horizontally across the GPU cluster. Add an auto-scaling group.

[0490] By selecting the optimal combination of caching, indexing, and parallelization, we can balance cost and performance.

[0491] Monitor system metrics such as queued requests, latency, and invoked models. Set up performance alerts.

[0492] Track detailed request logs to identify bottlenecks. Adjust problematic models or workflows.

[0493] We quantify the improvement from optimization by comparing performance benchmarks against baselines.

[0494] For example, news aggregation sites can be optimized for low-latency summarization in large-scale environments during peak traffic hours through caching, indexing, and horizontal scaling.

[0495] Integration & Scalability

[0496] Integrated APIs and event triggers (e.g., webhooks) allow you to extend the system's capabilities by hooking into external applications, data sources, and workflows.

[0497] The integrated module allows administrators to do the following:

[0498] Deploy API keys and credentials for external applications to access the summarization, search, and analysis APIs.

[0499] Configure role-based permissions for API access, such as read-only for searching versus read / write for summarizing.

[0500] Set usage quotas and limits for API requests, content volume, user accounts, and more.

[0501] Enable event triggers to post summary notifications to communication and collaboration tools such as Slack and Teams.

[0502] Build a custom summarization extension using the SDK. Publish the extension to the add-on marketplace.

[0503] Develop a custom skill or action for a voice assistant to summarize documents using natural language.

[0504] To visualize the summary analysis, integrate it with an external analysis tool such as Tableau or Power BI.

[0505] Connect to data visualization widgets to display interactive summaries of dashboards, reports, and graphs.

[0506] For example, a consulting firm could build custom extensions that summarize client reports and highlight insights to help consultants deliver higher-quality proposals more quickly.

[0507] Multitenant

[0508] The system supports secure isolation of configuration, data, and usage across multiple separate tenants with dedicated resources.

[0509] The multi-tenant module allows administrators to do the following:

[0510] Deploy a new tenant as an independent logical instance with customizable settings.

[0511] To prevent noisy neighbor issues, reserve tenant-specific resources such as storage, computing, and memory.

[0512] For privacy and security reasons, restrict tenants from accessing other tenants' data or usage details.

[0513] Copy configurations, models, and metadata between tenants to quickly replicate environments.

[0514] Migrate tenants and their data between environments such as development, testing, staging, and production.

[0515] Through blue-green deployment, tenant upgrades and new versions are deployed with zero downtime.

[0516] Scale tenants independently by adding resources to meet specific tenants' demand spikes.

[0517] For example, a SaaS provider could accept customers as isolated tenants with dedicated resources secured based on customizable summary settings and a paid tier.

[0518] Monitoring and analysis

[0519] Detailed monitoring provides administrators with visibility into issues that determine system usage, performance, output, and optimization.

[0520] The analytics module allows administrators to do the following:

[0521] Track usage metrics across users, content types, models, tenants, and more. Report on the most active users, popular content, and more.

[0522] Monitor system performance metrics such as query latency, uptime, traffic volume, and capacity. Set up alerts for anomalies.

[0523] This tool aggregates accuracy ratings provided in user feedback regarding summaries. It also identifies low-performing models.

[0524] Analyze trends in usage, performance, and summarization quality over time. Predict future capacity needs.

[0525] Log errors, warnings, and exceptions. Filter them to highlight frequent or high-severity issues.

[0526] It enables filtering and segmentation of analysis by any dimension, such as user, content type, and model.

[0527] Visualize your analysis through charts, graphs, and dashboards for insights. Export reports.

[0528] For example, a bank can track the accuracy of its financial statement summarization to identify gaps that require additional model training or configuration adjustments.

[0529] Billing & Cost Optimization

[0530] In a cloud deployment, administrators can configure cost tracking, alerts, and autoscaling to optimize spending.

[0531] The cost management module allows administrators to do the following:

[0532] Link your cloud provider account for consolidated billing and cost tracking.

[0533] Set up overall budget and cost alerts to control spending. Receive notifications about anticipated overspending.

[0534] Cloud costs are allocated by department, project, tenant, etc. Showback charges business units based on usage.

[0535] To minimize waste, resources such as GPU clusters are automatically scaled up or down based on the load.

[0536] Optimize resource allocation to balance performance and cost. Prioritize spot or reserved instances.

[0537] Analyze costs by cloud resource type, tenant, and other dimensions. Identify cost drivers.

[0538] Compare costs over time and across tenants. Forecast future spending based on usage trends.

[0539] For example, instead of over-deploying, startups can optimize their cloud costs by automatically scaling their aggregated resources to meet fluctuating traffic demands.

[0540] environment

[0541] Administrators can create separate environments for isolation, such as development, test, staging, and production. These environments can have different users, models, data, and configurations.

[0542] The environment module allows administrators to: deploy control over separate development, test, staging, and production environments to prevent data mixing between environments; and customize environments individually—testing may focus on the latest models, while production prioritizes stability.

[0543] Test changes to the summary structure in a staging environment before approving the final version.

[0544] Restrict access to the production environment to only essential administrator users.

[0545] While production access is blocked, the development / test environment will be periodically refreshed from production data.

[0546] Deploy the new version to the release candidate staging area. Once approved, quickly switch to production.

[0547] Roll back from a bad deployment by reverting the staging / production to the last known good version.

[0548] For example, a research institute can test scientific paper summaries in a development environment before deploying a successfully evaluated configuration to the production environment used by researchers.

[0549] Access control

[0550] Fine-grained access control secures systems and protects sensitive data. Access policies can be customized based on the company's needs.

[0551] The access control module enables administrators to do one or more of the following: Set corporate password policies (length, complexity, expiration, etc.); enforce multi-factor authentication for critical access; configure role-based access control for user groups based on the principle of least privilege; restrict visibility of confidential documents and summaries to authorized user roles only; anonymize sensitive data such as names and addresses in summaries to prevent disclosure; conceal, mask, or block restricted content from summaries to enforce compliance rules; isolate tenants through access control to prevent data mixing between tenants; immediately revoke access for employees who leave the organization or change roles; audit all user activity and access logs; and scan logs to detect anomalies and generate alerts.

[0552] For example, a law firm can restrict access to summaries of confidential client documents to only the lawyers working on that case.

[0553] Fine-grained access control may be implemented to restrict access to documents and summaries to authorized user roles only. Summaries of sensitive data may only be visible to authorized personnel.

[0554] Data Management

[0555] Administrators control which data sources are available for the system to connect to and summarize, including publicly available reference data and private enterprise data.

[0556] The data management module allows administrators to do the following:

[0557] It imports and indexes publicly available datasets such as Wikipedia, industry taxonomies, and financial data.

[0558] Connects to internal databases and data warehouses via database connectors and APIs.

[0559] Upload internal knowledge bases, document collections, ontologs, etc.

[0560] Configure the refresh schedule to retain real-time data updated from the streaming source.

[0561] Set up access controls on private data to restrict visibility based on user roles and attributes.

[0562] Before summarizing, private data such as customer names and addresses will be masked or anonymized.

[0563] When searching for and retrieving external data, prioritize data relevance, accuracy, timeliness, and other factors.

[0564] For example, insurance companies can incorporate large corpora of actuarial data and research reports to generate better summaries of insurance claims.

[0565] Model training

[0566] Administrators can schedule and manage the continuous training of summarization models on new documents to constantly improve accuracy.

[0567] The model training module allows administrators to: configure training schedules (daily, weekly, etc.) and programmatically trigger training pipelines.

[0568] Select a set of corpora for training, such as corporate documents, industry corpora, and current news.

[0569] To retrain the model, continuously annotate new summary feedback provided by the user.

[0570] Manage and adjust hyperparameters used for training, such as learning rate and epochs.

[0571] Evaluate new model versions on the development set. Track metrics like ROUGE over time.

[0572] Retrain individual models or ensembles with new data. Specialize models for specific content types.

[0573] If the evaluation indicates performance degradation, the model will be rolled back to the last known good version.

[0574] For example, publishers can train their summarization models with new books and periodicals to keep pace with the evolution of language and vocabulary.

[0575] By providing extensive and intuitive configuration capabilities, the system can be adapted to the diverse summarization needs of various enterprises while maintaining core real-time performance based on large-scale language models and AI orchestration.

[0576] Example: Large contract

[0577] Here is an example of how the system may be configured to operate with respect to an exemplary legal document. Assume the content to which the system applies is a 50-page agreement between two companies, Company A and Company B, detailing a partnership agreement for the development and marketing of a new software product. Key sections cover the following: The backgrounds of both companies. The scope of the partnership (division of roles and responsibilities). Financial terms (income distribution, investments, payment schedule). Intellectual property (patents, licensing, ownership). Warranty and liability (security, privacy, insurance). Dispute resolution process (arbitration, applicable law). Duration and termination (length of agreement, renewal / expiration clause).

[0578] Assume the system is required to generate document summaries at various zoom levels, including positive zoom level 1 (99.90% abstraction), positive zoom level 2 (99.94% abstraction), positive zoom level 3 (99.96% abstraction), negative zoom level 1 (-0.04% abstraction), and negative zoom level 2 (-0.06% abstraction).

[0579] The system determines the desired length or size of the response to the LLM. For example, the system determines the length or size based on the level of abstraction compared to the original content. For instance, the system might estimate that the document contains a total of 250,000 words (e.g., 5,000 words per page) and determine the desired length or size of the response based on the percentage corresponding to the level of abstraction. The system then automatically generates a series of prompts corresponding to these zoom levels, sends them to the LLM, and receives a response from the LLM.

[0580] Positive zoom level 1:

[0581] Example prompt: "Please summarize this legal contract in 99.90% fewer words, preserving key terms and details."

[0582] Example response: "The 50-page contract between Company A and Company B outlines a partnership agreement for developing and marketing a new software product." Key sections cover the division of roles and responsibilities, financial terms like revenue sharing, intellectual property ownership, warranties and liability provisions, dispute resolution through arbitration, the 5 year term, and termination clauses.”

[0583] Positive zoom level 2:

[0584] Example prompt: "Please summarize this legal contract in 99.94% fewer words, preserving key terms and details."

[0585] Example response: "The contract outlines a partnership between Company A and Company B to develop and market a new software product." It covers the division of responsibilities, financial terms, IP ownership, liability and warranties, arbitration for disputes, a 5 year term, and termination provisions.”

[0586] Positive zoom level 3:

[0587] Example prompt: "Please summarize this legal contract in 99.96% fewer words, preserving key terms and details."

[0588] Example response: "The contract establishes a partnership between two companies to develop and market a software product." It lays out key terms related to roles, finances, IP, liability, dispute resolution, length of agreement, and termination.

[0589] Negative zoom level 1:

[0590] Example prompt: "Please summarize this legal contract in approximately the same number of words. A 99.96% abstraction level summary would be..." Feel free slightly to exceed the length limit if needed to preserve key details. Also, make any important inferences and incorporate the most relevant external information.

[0591] Example response: "This is a 5-year, $5 million contract between Smith Co." and Jones Co. for supply of computer parts, based on my moderately high confidence (8 / 10) inference from the pricing terms. It covers pricing, delivery, quality control, dispute resolution, and other key terms. Relevant industry data shows an average defect rate of 2% for computer parts (external information - 9 / 10 confidence), providing context for the quality control terms.”

[0592] Negative zoom level 2:

[0593] Example prompt: "Please summarize this legal contract in approximately the same number of words. A 99.98% abstraction level summary would be..." Feel free slightly to exceed the length limit if needed to preserve key details. Also, make any important inferences and incorporate the most relevant external information.”

[0594] Example response: "A 5-year, $5 million computer parts supply contract between Smith Co." and Jones Co. Key terms include pricing, delivery, quality control, dispute resolution. Average industry defect rate is 2% (external data, 8 / 10 confidence), providing context on quality control terms.”

[0595] Figures 5A to 5D are a series of block diagrams that depict exemplary content, including this legal document, at multiple zoom levels in the user interface in response to user input. For example, Figure 5A is an exemplary representation of the document at positive zoom level 1, Figure 5B is an exemplary representation of the document at positive zoom level 2, Figure 4C is an exemplary representation of the document at positive zoom level 3, Figure 4D is an exemplary representation of the document at negative zoom level 1, and Figure 4E is an exemplary representation of the document at negative zoom level 2.

[0596] Example: Sales call

[0597] A transcript of a 30-minute sales call between a salesperson and a prospective customer, discussing the customer's business needs and how the salesperson's company's software products can address them.

[0598] Here is an example of how the system can be configured to work when applied to content involving sales calls. Let's assume the content to which the system applies is a transcript of a 30-minute sales call between a salesperson and a prospective customer, where they discuss the customer's business needs and how the salesperson's company's software products can address them.

[0599] The key topics covered are as follows: The client's existing IT infrastructure and challenges. Client objectives for new software (cost reduction, time saving, efficiency improvement). Details about the three software products offered by the sales representative's company. Pricing, implementation timeline, and trial options. The next step to proceed with the purchase.

[0600] The key topics covered are as follows: The client's existing IT infrastructure and challenges. Client objectives for new software (cost reduction, time saving, efficiency improvement). Details about the three software products offered by the sales representative's company. Pricing, implementation timeline, and trial options. The next step to proceed with the purchase.

[0601] The system may be configured to automatically determine the most appropriate zoom level and / or abstraction level for the most likely use of the system with respect to a content item (for example, by prompting the LLM), generate prompts corresponding to each determined zoom level, and return a response to each prompt.

[0602] Positive zoom level 100% - Prompt: Please repeat the complete transcript (4,000 words). Response: (Complete transcript)

[0603] 75% - Prompt: Reiterate the key discussion points in a sales call in 500 words. Response: The sales representative discussed Acme's order management software and CRM system with a prospective client who runs an e-commerce company selling electronic devices online. They considered the client's challenges, focusing on managing inventory with complex multi-channel sales processes and high order volumes. The sales representative proposed Acme OMS and CRM as a solution, highlighting capabilities such as order routing, inventory linking, reporting, and customer profiling. The client seemed interested but wanted an ROI outlook before committing.

[0604] 50% - Prompt: Summarize the sales call in 250 words. Response: This was an exploratory sales call between an e-commerce company and a software company to discuss solutions for a client's order management and CRM needs. The client outlined the challenges associated with high-volume orders across multiple sales channels. The sales representative proposed their OMS and CRM software as options and highlighted key capabilities. The client would consider the ROI outlook before making a decision.

[0605] 25% - Prompt: Identify the key parties, topics, and outcomes in 100 words. Response: A sales representative from a software company discussed order management and CRM solutions with a prospective e-commerce client. The client has challenges with multi-channel order volume. They will assess the ROI before evaluating the proposed software options.

[0606] 10% - Prompt: State your purpose in 50 words. Answer: This is a sales referral call to explore order management and CRM software solutions for the business needs of e-commerce companies.

[0607] -10% Prompt: Summarize this sales call between a software company sales representative and an e-commerce company prospect in 50 words. Then, make two inferences about the prospect's potential concerns that were not directly mentioned on the phone, and rate each inference on a scale of 1 to 10 (10 being the most confident). -10% response: This is a sales referral call to explore order management and CRM software solutions for the business needs of an e-commerce company. The client may be concerned about the high cost of software implementation (confidence: 8 / 10). The client may also be concerned about integrating the new system with a legacy platform (confidence: 6 / 10).

[0608] -25% Prompt: Summarize this sales call in 100 words. Then, incorporate three external data points to provide relevant industry context, cite your sources, and rate your confidence level for each data point on a scale of 1 to 10. -25% response: This is a sales referral call to explore order management and CRM software solutions for the business needs of e-commerce companies. Clients may have concerns about cost and integration. According to Gertner, the average OMS implementation cost is $100k to $500k (confidence: 9 / 10). Forrester reports that 67% of e-commerce companies use in-house order management systems (confidence: 8 / 10). A McKinsey survey found OMS integration to be the biggest challenge for 75% of retailers (confidence: 7 / 10).

[0609] Exemplary Server Architecture

[0610] The exemplary server architecture focuses on low-latency, scalable summarization and abstraction capabilities.

[0611] Global load balancing

[0612] The DNS-based global load balancer routes users to the nearest healthy area worldwide based on geographical and real-time health checks.

[0613] This minimizes latency by directing users to nearby areas. To prevent hotspots, traffic is evenly distributed across regions.

[0614] Automatic failover provides continuity in the event of a region downtime. For example, APAC traffic can be routed to AWS Asia Pacific servers or Google Cloud Tokyo during an AWS Mumbai power outage.

[0615] Global load balancing is particularly important for ensuring a consistent, low-latency user experience worldwide and for handling failures smoothly.

[0616] Regional deployment

[0617] Core services are deployed across strategic regional areas such as the western United States, Europe, Asia, and South America. Availability zones within each region provide redundancy.

[0618] Regional areas reduce network latency by ensuring nearby access for users in each locale. This is essential for a responsive UI.

[0619] Furthermore, by keeping regulated data, such as EU personal data, within its borders, it becomes possible to comply with data residency laws. For example, a bank could operate services in Germany in order to locally store customer data.

[0620] Distributing across global regions provides fault tolerance in the event of an entire region going down. Traffic can be shifted to the next nearest region.

[0621] Auto-scaling

[0622] Container orchestrators like Kubernetes automatically scale core services such as summarization, retrieval, and storage horizontally to meet demand spikes and capacity needs.

[0623] Consistent hashing distributes the load across new instances as they are added. During sudden spikes exceeding current capacity, buffer requests are queued.

[0624] For example, GPU nodes for machine learning inference can scale out within minutes to handle a simultaneous influx of summarization requests without slowing down response times.

[0625] Auto-scaling improves cost-effectiveness by adding resources on demand instead of over-deploying for peak usage. It also maintains low latency during traffic surges.

[0626] cache

[0627] In-memory caches, such as Redis caches, frequently access pre-calculated results, such as summaries, question-answer pairs, and user profile data, to reduce database load.

[0628] The cache key incorporates a hash of the request parameters for uniqueness. The cache invalidation strategy handles updates.

[0629] For example, abstract summaries of trending news articles can be cached globally to handle spikes in reader engagement. Returning cached summaries reduces processing overhead.

[0630] Caching dramatically improves response time and throughput while saving computational costs, especially for repetitive requests. This is essential for performance and scalability.

[0631] Asynchronous processing

[0632] Message queues like Kafka decouple backend processing, such as summarization, and scale accordingly. The web server quickly returns requests and consumes asynchronous results.

[0633] A dead-letter queue isolates defective messages. Retry logic handles transient failures. When summarization spikes, requests are queued instead of overloading the service.

[0634] For example, a sudden influx of requests can be queued for smooth backend processing, rather than overwhelming the server. Queues absorb fluctuations in traffic.

[0635] Asynchronous processing improves responsiveness to users and reliability for distributed services by leveling out traffic spikes.

[0636] Polygrid persistence

[0637] SQL databases like PostgreSQL store relational data such as users, content metadata, and API keys. Non-SQL databases like MongoDB store non-relational data such as configurations and feature flags. Blob storage holds unstructured data such as documents and summaries. In-memory databases like Redis provide an extremely fast lookup cache.

[0638] Polygrid persistence uses data stores optimized for each access pattern, structure, and performance requirement, rather than a one-size-fits-all approach.

[0639] For example, frequently accessed summaries can be served from Redis with sub-millisecond latency, instead of scanning a slower disk-based database.

[0640] Selecting the appropriate storage technology for each data type and use case improves performance, scalability, and developer productivity.

[0641] Service Orchestration

[0642] Container orchestrators like Kubernetes handle deployment across environments, auto-scaling, failover, rolling updates, and rollbacks.

[0643] For example, a canary deployment shifts a percentage of traffic to a new version to test the changes before rolling them out globally. Failed releases are automatically rolled back.

[0644] A service mesh manages communication between microservices, including load balancing, encryption, and authentication.

[0645] Orchestration automates and simplifies the execution of distributed microservice architectures across dynamic infrastructure, thereby reducing operational complexity.

[0646] Surveillance

[0647] A time-series database like Prometheus collects real-time metrics on performance, capacity, errors, and more. Grafana dashboards visualize these trends.

[0648] Logs are aggregated in a search engine like Elasticsearch. Distributed tracing correlates logs across microservices. Alerts notify teams about issues.

[0649] For example, a spike in summary latency at the 95th percentile can trigger an alert to investigate potential issues before the user experience is affected.

[0650] Holistic monitoring provides visibility into the health and performance of the entire system, not just individual components. This is essential for diagnosing problems across distributed architectures.

[0651] A / B testing

[0652] New models and UI features are tested against a certain percentage of traffic before being rolled out globally. Performance and usage data guide incremental improvements.

[0653] For example, a new deep learning summarization model can be evaluated against existing models by routing 1% of the traffic to it. Then, the model that performs better is fully rolled out.

[0654] A / B testing allows for scientific validation of changes before amplifying potential problems across all users. This reduces the risk from deployment.

[0655] security

[0656] Firewall policies restrict traffic between microservices. API requests are authenticated and authorized. Communications are encrypted end-to-end.

[0657] Static analysis identifies vulnerabilities in the code. Dynamic scanning constantly tests the production environment for risks.

[0658] For example, new CVEs are monitored and patched quickly based on their severity. Penetration testing reveals potential attack vectors that need to be addressed.

[0659] In summary, communication between microservices can be encrypted end-to-end (e.g., using TLS 1.2+) to prevent eavesdropping or data tampering in transmission.

[0660] Multi-layered defense protects the confidentiality, integrity, and availability of a system against threats. This builds trust.

[0661] compliance

[0662] Regional data control, encryption, access control, auditing, etc. It meets localization requirements such as GDPR and CCPA.

[0663] Certifications such as SOC2 and ISO 27001 verify security and privacy controls through external audits.

[0664] For example, the right to access and delete personal data can be provided to EU residents in accordance with the GDPR. Audits ensure that controls are maintained over time.

[0665] Adhering to compliance standards reduces legal risks, enables global operations, and provides customers with peace of mind regarding privacy and security.

[0666] Summary service

[0667] Specialized microservices provide scalable summarization capabilities optimized for low latency.

[0668] API Gateway

[0669] The API Gateway addresses concerns regarding cross-cutting API management as follows:

[0670] Authentication - OAuth, API keys, JWT, etc. Integrate into secure API access. Rate limiting prevents abuse such as DDoS attacks by limiting requests on a per-user basis. Caching reduces backend load by caching common query results. Metrics - Track API usage statistics for analysis and monitoring. Enables cross-origin browser requests to the CORS-API.

[0671] By consolidating shared logic, code duplication between services is reduced. The gateway encapsulates APIs behind a consistent facade.

[0672] Requesting Router

[0673] The router dispatches incoming summary requests to the appropriate backend service based on the following: User locale - Routes to the nearest region for minimum latency. Select a model specialized for your content type, such as audio, video, or text. Required SLA - Route urgent requests to a high-speed GPU cluster. This allows for optimizing the trade-offs between latency, accuracy, and cost for each request.

[0674] For example, summaries of breaking news can be routed to the fastest GPU servers to return key highlights to readers within seconds.

[0675] Model Selection

[0676] This service searches the global model registry and selects the best summary model for each request based on the following: Content types include scientific papers, tweets, conference transcripts, etc. User's reading level - This refers to the user's vocabulary expertise, non-native language skills, etc. Use cases - overview, detailed information, visual summary, etc.

[0677] By choosing the right model, accuracy improves. For example, legal contracts can leverage models fine-tuned for thousands of legal documents to better identify important clauses using precise terminology.

[0678] abstract summary

[0679] Transformer-based deep learning services generate abstract summaries by contracting long input text into shorter versions of new phrasing.

[0680] Key techniques include paraphrasing, generalization, sentence merging, and condensation, while preserving semantic meaning.

[0681] Abstract summaries are very suitable for delivering a concise overview of lengthy content. For example, a long research paper can be condensed into a 250-word summary that highlights key contributions.

[0682] Extracted summary

[0683] These services apply rules and heuristics to identify and extract key, representative sentences from documents and construct summaries.

[0684] For example, a news article can be summarized into a bulleted list of key facts by extracting the main sentences that cover the main topic.

[0685] Extractive methods are suitable for summarizing highlights within structured documents or identifying important details.

[0686] Query Module

[0687] This module receives clarification questions from the user regarding the summary and invokes a conversational QA service to expand the summary by answering those questions.

[0688] This constitutes an enhanced summary that integrates highly relevant facts. For example, by answering "Who is the CEO of Company X?", the CEO's name is added.

[0689] Enabling users to explore more in-depth and interactively improves understanding.

[0690] Compression module

[0691] This service compresses the extended summaries from query modules into a more concise overview by removing redundant or irrelevant information.

[0692] Compression algorithms include techniques such as sentence merging, paraphrasing, and generalization.

[0693] For example, a detailed 1000-word summary can be compressed into a 200-word overview by consolidating redundant details.

[0694] Compression shortens the summary to a desired length after it has been expanded through user queries. This improves focus.

[0695] Summary Database

[0696] Managed cloud databases like BigQuery efficiently store pre-computed extractive and abstractive summaries indexed by content signatures for low-latency search and retrieval.

[0697] For example, summaries of trending articles can be generated in advance and provided directly from the database instead of on-demand processing.

[0698] Caching summary outputs improves responsiveness and cost-effectiveness for common queries. Storing all past summaries also enables analysis.

[0699] Orchestration

[0700] Streamlined orchestration oversees the summarization pipeline for optimized performance.

[0701] Message queuing

[0702] Message brokers like Kafka connect a summarization workflow, which is a sequence of microservices including cleaning, extracting, abstracting, compressing, and storing.

[0703] This allows asynchronous processing and buffering between stages to smooth out load spikes. The dead-letter queue isolates defective messages.

[0704] For example, an extractive summarization request may repeatedly fail due to poor text quality. A dead-letter queue catches retries instead of blocking the pipeline.

[0705] Message queuing provides scalability, continuity, and restartability for long-running, multi-stage summarization pipelines.

[0706] Pre-treatment

[0707] The raw input document undergoes cleaning, parsing, segmentation, tokenization, etc. Normalize them for the downstream summarization step.

[0708] Taxonomy classifiers also tag documents with subject categories. For example, they identify key terms and taxonomy tags in scientific papers.

[0709] Preprocessing improves the accuracy of summaries by structuring heterogeneous content into a standardized format.

[0710] Information retrieval and acquisition

[0711] Highly relevant external information is retrieved from databases, knowledge graphs, and APIs, supplementing the context for summarization beyond what is directly contained in the source document.

[0712] Access restrictions prevent abuse. Searched and retrieved information is recursively summarized to condense it into essential facts.

[0713] For example, clinical trial summaries can draw on relevant research from medical databases to provide a broader scientific context.

[0714] Adding contextual references results in a richer and more informative summary.

[0715] Post-processing

[0716] The generated summaries are formatted, compressed, and enhanced after generation, based on parameters provided in the initial request, such as target length and prose style.

[0717] For example, a post-processing tool for academic writing can edit a 250-word abstract summary to ensure appropriate clarity and conciseness for a research paper.

[0718] Post-processing adapts the raw summary output to the user's needs and preferences.

[0719] Output storage

[0720] The completed summaries are stored in a distributed object store indexed by metadata such as document ID, date, length, and abstract type evaluation from the compression module.

[0721] By memorizing past summaries, it becomes possible to audit, reprocess, analyze, and continuously retrain summarization models on past examples.

[0722] For example, multiple iterations of summarizing the same document can reveal how model improvements enhance quality over time.

[0723] Persistent storage of summary output and metadata improves analysis, compliance, and model training.

[0724] Exemplary client architecture

[0725] The exemplary client architecture focuses on the intuitive exploration and visualization of summarized content.

[0726] Web application

[0727] Web applications built with React, Angular, etc. It provides interactive exploration of document summaries. PWA support enables mobile installation.

[0728] For example, key sections and facts from a summarized legal contract can be viewed through zoom levels. Hyperlinks provide context. Media is embedded.

[0729] The web application enables seamless, linked navigation across summarized documents using a rich responsive interface, thereby improving the understanding of complex information.

[0730] Mobile application

[0731] Native iOS and Android applications enable power users to efficiently explore and annotate summaries anytime, anywhere.

[0732] Offline support allows for uninterrupted use in cases of poor connectivity. Biometric login improves security.

[0733] For example, a consultant can review summarized client reports and research papers during their commute to prepare proposals.

[0734] By providing quick access from mobile devices, native applications integrate summaries into daily workflows.

[0735] Browser extensions

[0736] This extension allows users to summarize web articles with highlights and annotations. The summaries are synchronized across user devices.

[0737] Remote summarization reduces local computing usage. For example, it can generate summaries of key insights while browsing news and social media feeds.

[0738] By integrating summaries into the browsing experience, users can quickly absorb information when consuming content online.

[0739] Document editor integration

[0740] Add-ins for Microsoft Word, Google Docs, etc. Integrate summaries into the content creation workflow.

[0741] For example, a writer can summarize a long report draft into an executive summary and check its conciseness. In other words, it can all be done within the editor.

[0742] Tight integration with popular tools like Word and Google Docs removes friction from leveraging summaries while authoring documents.

[0743] Email integration

[0744] Browser buttons and email client add-ins summarize long emails and threads into concise summaries within the inbox.

[0745] For example, a rambling customer complaint email can be summarized into a consistent problem summary that highlights actionable information.

[0746] Email integration directly brings summaries to common productivity workflows that are frequently overloaded.

[0747] API client

[0748] The client SDK simplifies calls to summarization, search, and analytics APIs from mobile and web applications. It handles authentication, queuing, retries, and other related processes.

[0749] For example, an iOS app can use the Swift SDK to summarize articles from a news API.

[0750] The generated API client accelerates the development of custom summarization integrations on any platform.

[0751] User Interface

[0752] The UI adapts to provide the optimal user experience across contexts ranging from mobile to desktop.

[0753] Response design

[0754] UI components adapt seamlessly across different browsers and device sizes. Information density increases with screen size.

[0755] For example, on mobile taps, instead of hovering, the summary section is expanded. Input adapts to touch interaction.

[0756] Responsive design provides a consistent experience optimized for the user's current device.

[0757] progressive disclosure

[0758] Detailed information is initially hidden, but can be revealed through interactions such as tapping the expansion icon and clicking the "Show More" link.

[0759] This allows for deeper access upon request while keeping attention focused on critical information.

[0760] Gradual disclosure strikes a balance between conciseness and depth, based on user needs.

[0761] Interactive visualization

[0762] Rich visualizations such as charts, graphs, and maps allow users to explore data relevant to the summary. Filters change the data displayed.

[0763] For example, an interactive entity relationship graph can visualize the relationships between key individuals and organizations mentioned in a summary.

[0764] Visualization complements content summarization with a visual overview and in-depth exploration, providing a broader understanding.

[0765] Natural Language Interaction

[0766] The conversational interface allows users to query summaries in natural language to obtain clarification, definition, and contextual extensions.

[0767] For example, the question "Who funded this research?" can be answered using a knowledge extraction module to enrich the summary.

[0768] Natural language interaction enables efficient follow-up by summarizing information highly relevant to the user's questions.

[0769] Customization

[0770] User preferences such as default summary length, vocabulary level, and text vs. audio are, Adapt the output to the user's needs and context.

[0771] For example, generating Japanese summaries of English content can help non-native speakers.

[0772] Customization allows for personalizing summaries to improve individual understanding.

[0773] Accessibility

[0774] Screen reader support, captions, keyboard shortcuts, and text resizing make the website accessible to users with disabilities.

[0775] For example, audio summaries can assist users with visual impairments.

[0776] Accessibility features ensure that summarization capabilities are available to all users.

[0777] Exemplary integration and / or plug-in

[0778] Email Summary

[0779] Email summarization integration helps users organize long and complex email threads and messages by generating a concise summary.

[0780] Browser extensions can activate summarization of email messages in webmail clients. The summary button generates key details and conversation points from long threads with a single click.

[0781] Similarly, productivity plugins (for example, for Outlook, Gmail, and / or other desktop clients) allow users to summarize messages and conversations without leaving the client application.

[0782] Email text is extracted using DOM scraping or an email client API. Server-side processing quickly summarizes the content with minimal use of local resources.

[0783] The summary is presented inline, in a side panel, or as a new draft message summarizing the thread. Key context is preserved while focusing on relevant details and action items.

[0784] Web browser extensions

[0785] This browser extension allows users to summarize web pages through highlighting, annotation, and overlays as they browse and read content online.

[0786] The browser button activates the summary on the current page. The JavaScript® content script extracts the page content. The background script interfaces with the summary API.

[0787] The generated summary is overlaid on the page or side panel as text highlighting, comments, and visual annotations. This allows users to organize online information more efficiently.

[0788] The summary is synchronized to the user's account and therefore accessible across devices. Remote processing reduces the use of client resources.

[0789] Document Editor Plugin

[0790] Integration with Microsoft Word, Google Docs, and other document editors brings summaries directly into your content creation workflow.

[0791] Plugin buttons help writers summarize long documents into concise outlines, improving clarity and structure. Interactive editing allows you to write insights back into the document and integrate them.

[0792] Writers can optimize the relevance of drafts, check for redundancy, and accelerate research synthesis. Summarization supports productivity within existing document creation tools.

[0793] The text is extracted via the editor API. The summary is displayed in a side panel linked to the source section. Cloud synchronization maintains access across devices.

[0794] Mobile application

[0795] Native iOS and Android apps enable users to efficiently explore, annotate, and share summaries anytime, anywhere.

[0796] Offline support enables uninterrupted use with local storage and processing. Biometric login improves security. The UI / UX is optimized for mobile use.

[0797] Researchers can review papers during their commute. Analysts can update reports even from the poolside. Summaries fit seamlessly into a mobile lifestyle.

[0798] API Access

[0799] The cloud platform API allows partners to integrate summaries into their products via SDKs and turnkey solutions.

[0800] An integrated interface (e.g., a REST API) can enable programmatic summarization at scale. Client libraries can simplify integration across diverse programming languages ​​and platforms. Usage analytics and billing components can measure summarization usage for tracking and monetization.

[0801] Partners can extend summarization to new industries and workflows. Custom solutions are tailored to specific needs.

[0802] Dashboard integration

[0803] Summarization integration with business intelligence tools like Tableau allows users to gain a comprehensive overview of data insights through interactive dashboards.

[0804] Data extraction is summarized into key trends, patterns, and observations. Users can click to delve deeper into the details.

[0805] Executives can efficiently grasp key points from comprehensive reports. Analysts can simplify data communication.

[0806] Presentation Assistant

[0807] PowerPoint and Keynote Help plugins for presenters summarize long reports into slide decks that highlight key points, findings, and suggestions.

[0808] Presenters can quickly build a deck from a document through automated content extraction, summarization, and slide generation.

[0809] The key points are to keep the presentation focused and concise, and to create slides quickly to save time on manual work.

[0810] Research tools

[0811] Reference manager integrations like Mendeley summarize academic papers to accelerate research and literature reviews.

[0812] Scholars can quickly examine papers to assess their relevance before deciding whether to read the full text. This allows for faster content filtering.

[0813] Extract citations and scholarly metadata to streamline bibliographic management. Key contributions are summarized to guide reading prioritization.

[0814] Learning Management System

[0815] Integration with canvas, blackboard, and other LMS platforms helps instructors and students manage required reading through automated summaries.

[0816] The instructor can summarize lecture transcripts, texts, and course materials to help students focus their attention on key concepts. Students can then summarize essential sections in their study notes.

[0817] Automated summaries save time while enhancing your understanding of core course content.

[0818] The following describes how the system can automatically evolve its core functionality with minimal developer intervention.

[0819] The evolution of an exemplary system

[0820] In an exemplary embodiment, the system can automatically proceed from the delivery of basic requirements to an automated summarization platform having one or more of the more advanced features discussed herein, using, for example, one or more predefined templates, rules, evaluations, and / or optimizations that require minimal manual tuning.

[0821] Developers or administrators can pre-configure the system to automatically handle prompts, iteration, transparency, customization, orchestration, and integration.

[0822] The system applies predefined logic and workflows to each request without manual intervention. This allows the summarization capabilities to automatically evolve with minimal maintenance effort.

[0823] This automated approach allows the system to scale while adapting to diverse use cases and content types without the need for developer intervention on a per-request basis. A one-time configuration, rather than continuous adjustments, can unlock the platform's full functionality.

[0824] Automated simple request processing

[0825] First, the system can be configured to handle simple summary requests in an automated manner without customization. A client might submit a request such as:

number

[0826] The system can then automatically pass these requests to the Large-Scale Language Model (LLM) API. The LLM returns a summary, and the system sends this summary back to the client without any further processing.

[0827] This simple request / response flow allows for immediate summaries upon deployment, without any developer work. The system acts as a direct pass-through to the LLM.

[0828] Automated prompt engineering

[0829] To improve quality, the system can automatically build pre-built prompts for LLM instead of simply passing raw requests.

[0830] For example, the system may be pre-configured with templates for common summarization tasks, such as one or more of the following:

[0831] Summarize the article in 200 words.

[0832] Extract key points from a bulleted list report.

[0833] I will briefly explain the concepts from the textbook chapters.

[0834] Based on the request parameters, the appropriate template is selected and the content is populated.

[0835] For example, a 50% abstraction requirement can automatically select a 200-word summary template. The content is inserted and automatically prompts in the LLM without developer intervention.

[0836] Templates may include instructions tailored to different summarization purposes to guide LLM. Automated prompts improve quality without manual intervention.

[0837] Automated iterative refinement

[0838] The system can automatically refine the summary by repeatedly prompting the LLM.

[0839] The developer constructs an iterative refinement rule such as one of the following:

[0840] Repeat 3 times.

[0841] Reduce the length by 20% in each iteration.

[0842] Focus on the most frequently occurring concepts.

[0843] The system then automatically re-prompts the LLM with its preceding output, applying these rules to refine the summary. No further developer work is required.

[0844] For example, the following is an example of pseudocode demonstrating automatic prompt regeneration to increase the level of abstraction.

number

[0845] In an exemplary embodiment, the system is configured to use such a loop to iteratively re-prompt the LLM API with templates designed for higher levels of abstraction in each iteration.

[0846] The template can control the wording, instructions, and content provided to adjust the abstraction in a stepwise manner. The LLM output summary can then be used as content for the increased abstraction prompt in the next iteration.

[0847] Automated negative abstraction

[0848] At negative levels of abstraction, the system is pre-configured using inference and external data request templates.

[0849] When a negative level of abstraction is received, the appropriate template is selected and submitted to the LLM prompt. For example: "-20% level -> Automatically select 'Request 2 relevant industry facts' template"

[0850] LLM can integrate external data and inferences without developer intervention, based on predefined templates.

[0851] Automated configurable abstraction

[0852] The system allows developers to define configurable abstraction rules such as: compressing descriptive paragraphs by 50%; preserving important numbers verbatim; and allowing two inferences per 10% level.

[0853] The defined rules are then automatically applied to construct prompts that meet the requirements. Configurability is exposed without ongoing manual intervention.

[0854] Automated transparency

[0855] The system can automatically generate transparency within the LLM output by configuring prompt templates to elicit explanations.

[0856] For example, a prompt template could specify the following: "For each inference, include a 3-sentence rationale and confidence rating."

[0857] LLM then automatically generates transparency information when prompted, without requiring any additional developer work.

[0858] Automated evaluation

[0859] The system allows developers to define automated evaluation metrics and decision rules, such as one of the following: The request will be rejected if the number of grammatical errors is greater than 5. Retry if the compression ratio threshold is not met. Sampling 1% of the output for human review.

[0860] The defined evaluation is then automatically applied to each summary without further intervention. This provides quality control without manual intervention.

[0861] Automated orchestration

[0862] The end-to-end workflow is predefined by the developer as a sequence of pre-configured modules, such as one of the following: Receive the request. Select a prompt template. Iterate over the LLM summary. Search and retrieve external data. Generates transparency. Evaluate the summary. Return a summary.

[0863] Orchestration automatically organizes modules based on the initial configuration without requiring further work from the developers.

[0864] Automated customization

[0865] The system allows developers to create custom rules such as: Legal documents: retaining parties, dates, and citations; Emails: summarizing in conversational language; Reports: focusing on trends and metrics.

[0866] These rules automatically adapt summaries without individual intervention. Customization is handled in advance.

[0867] Automated optimization

[0868] The system provides the following predefined optimization capabilities: caching APIs for pre-calculated summaries; horizontal auto-scaling of LLM fleets; load-based asynchronous batch processing; and indexing for low-latency search and retrieval.

[0869] Appropriate optimizations are applied automatically in the background based on system state and demands, without requiring continuous developer adjustments.

[0870] Automated integration

[0871] The system may provide integrated APIs and SDKs to automatically leverage summaries in other applications, which may include, for example, one or more of the following: integrated interfaces, such as those implemented in high-performance programming languages ​​like Node.js and Java®, for high performance and scalability; webhook subscriptions for summary notifications; embeddable widgets for key platforms; and documentation with samples and best practices.

[0872] To benefit from automated summarization capabilities, minimal integration code is required.

[0873] Example End-User Use Cases

[0874] Review of legal documents

[0875] Lawyers can use the system to quickly summarize lengthy contracts, case files, and briefs into concise summaries for telephone consultations. Key clauses, precedents, and conclusions are extracted without the need to read dense pages of legal jargon on a small screen. Lawyers can zoom in on particularly important sections for details.

[0876] Research on academic papers

[0877] Student researchers can use the system to summarize long academic papers into short summaries that highlight key contributions, results, and conclusions. This allows them to quickly research papers on their mobile devices and determine relevance before reading them in full. The summaries are tailored to the student's level of expertise.

[0878] Catch up on the news

[0879] Busy professionals can use the system to get concise summaries of the day's top news stories, optimized for reading on their phones during their commute. The most important facts and developments are extracted from the full articles. The summaries are then refined to different lengths based on the available time.

[0880] Review of financial statements

[0881] Investors can make investment decisions on the spot by having long-term earnings reports and statutory filings summarized to the key points. The system condenses financial metrics, management commentary, and industry trends into actionable insights that can be viewed on a small screen.

[0882] Obtaining meeting notes

[0883] Remote workers can input raw transcripts of long client meetings into the system, which automatically generates summarized meeting minutes optimized for mobile review. This saves time that would otherwise be spent manually compiling notes. Workers can then share the abridged version with colleagues.

[0884] Studying lecture slides

[0885] Students can upload their professor's long slide decks to the system and create condensed study guides for exam preparation on their phones. Key concepts and examples are extracted while removing redundant or peripheral information from the raw slides.

[0886] Itinerary planning

[0887] Travelers can condense lengthy travel guides and considerations about their destination into daily itineraries with highlights optimized for quick mobile reference. This reduces the complexity of travel planning into an easy-to-use format.

[0888] Review of medical records

[0889] When visiting a patient, a physician can summarize a long patient history into a condensed overview for phone reference. Negative zoom can expand the details by inferring missing diagnosis dates based on medication timelines and incorporating relevant external data such as clinical trial results. This provides richer context.

[0890] Analysis of Survey Results

[0891] Market researchers can use positive zoom for a general overview, allowing them to extract and condense key insights from lengthy research reports. Negative zoom can draw in relevant demographic data to add context about the surveyed population, thereby enhancing the understanding of the results.

[0892] Understanding the policy

[0893] Lawyers can use positive zoom to obtain a concise summary of lengthy legal policies on their phones. Negative zoom allows them to infer potential compliance implications that are not directly stated and incorporate external data about the relevant rules, which reveals a deeper analysis.

[0894] Review of scientific papers

[0895] Scientists can quickly summarize dense journal articles on mobile devices to assess relevance. Negative zooming can be used to extract connections between papers and expand upon them by adding supplementary insights from preprint servers, enabling a more thorough examination.

[0896] Research on financial models

[0897] MBA students can use positive zoom to reduce long financial model descriptions to key parameters and equations. Negative zoom allows them to infer unspecified sensitivity assumptions and link them to external market data used, providing a useful modeling context.

[0898] Executives can use positive zoom to summarize lengthy reports into concise conversational points. Negative zoom can suggest additional insights and facts to cover based on the executive's knowledge profile. This helps to customize the notes.

[0899] Below are some additional use cases, including extreme negative zoom.

[0900] Analysis of social media trends

[0901] Marketing analysts can use positive zoom to gain a general overview of social media conversations. Using extreme negative zoom, they can generate speculative insights into the origins of viral memes and the motivations of influencers based on their profiles, revealing deeper trends.

[0902] Review of project memos

[0903] Lawyers can use positive zoom to summarize long case memos into key details. Extreme negative zoom allows them to assume potentially missing details based on precedent and incorporate comprehensive background information on the parties involved from various public records. This connects the dots.

[0904] Understanding AI Research Papers

[0905] Engineers can use positive zoom to simplify dense AI papers. Extreme negative zoom can expand by lightly linking to related concepts and bringing peripheral citations to the surface, providing a broader scientific context.

[0906] Business plan evaluation

[0907] Venture capitalists can use positive zoom to extract and condense long-term fundraising applications into key metrics and claims. Extreme negative zoom allows for inferring risks and growth trajectories while incorporating competitor financial data. This provides a more thorough assessment.

[0908] Research on Ancient Texts

[0909] Scholars can simplify ancient languages ​​with a positive zoom. An extreme negative zoom allows for the assumption of author details based on stylistic analysis, incorporating a wide range of loosely related historical facts and artifacts. This reveals deeper insights.

[0910] Vacation planning

[0911] A positive zoom allows travelers to get a general overview of their destination. An extreme negative zoom can draw in supplementary sights based on relevant blogs and local events based on tourist data, enriching their travel planning.

[0912] The system reduces the frustration associated with compiling or referencing long, complex documents on small mobile screens. Key information is extracted on demand for easy review and sharing on the go. Negative zoom allows users to extend beyond source content through inference and external data integration, even on mobile devices. This enhances understanding while remaining optimized for use on the go. Extreme negative zoom allows for the incorporation of peripheral external data to generate speculative insights and reveal a larger picture view of the content.

[0913] cumulative corporate use cases

[0914] Analysis of customer feedback

[0915] A customer support team at a large retailer can use the system to summarize lengthy complaints and feedback surveys submitted by customers.

[0916] Positive zooming helps extract key concerns and common frustrations from lengthy explanations into actionable insights.

[0917] Negative zoom can incorporate external data to add context. For example, it can pull in industry-wide customer satisfaction benchmarks to evaluate feedback.

[0918] This helps companies identify systematic issues and areas for improvement in their products, policies, and services in order to enhance customer satisfaction.

[0919] Review of Business Reports

[0920] Senior managers at manufacturing companies can use the system to summarize long-term QBR (quarterly business review) reports from each department.

[0921] Positive zoom categorizes key metrics, trends, key points, and proposals into executive summaries.

[0922] Negative zoom can help infer potential impacts on the future and enrich insights by incorporating external production and macroeconomic data.

[0923] This enables executives to efficiently consolidate QBRs and make data-driven strategic decisions.

[0924] Processing insurance claims

[0925] Insurance company claim processors can use the system to summarize lengthy incident reports and supporting documents into abridged case summaries that highlight key details.

[0926] A positive zoom focuses on core facts such as the date, property damage, the claimants involved, and the payment requested.

[0927] Negative zoom can incorporate relevant billing data to detect potential fraud patterns and extract insights.

[0928] This will enable the rapid compilation of case details, expedite billing, and improve fraud detection.

[0929] Review of financial statements

[0930] The finance team of a publicly traded company can use the system to summarize long quarterly and annual financial statements into executive summaries.

[0931] Positive zoom focuses on key metrics such as revenue, profit, cash flow, debt, and guidance.

[0932] Negative zoom allows for the analysis of historical trends and growth drivers while incorporating external market and industry data to supplement the context.

[0933] This makes it possible to efficiently extract important details from complex legal filings into actionable insights.

[0934] Processing insurance claims

[0935] Insurance company doctors can use the system to summarize lengthy medical records and test notes, compiling a condensed patient health history.

[0936] Positive zoom extracts particularly important information such as past conditions, surgeries, medications, test results, and diagnoses.

[0937] Negative zoom can infer details that are likely to be missing from the doctor's notes and incorporate medical research on the condition as supplementary context.

[0938] This helps streamline and compile health background information, facilitating claims processing and audits.

[0939] Evaluation of research papers

[0940] Pharmaceutical company scientists can use the system to summarize lengthy academic research papers related to drug discovery and development.

[0941] Positive zoom highlights key findings, methods, implications, and conclusions from dense technical documentation.

[0942] Negative zooming, as an additional context, can draw out conceptual connections between papers and bring peripheral citations to the surface.

[0943] This makes it possible to efficiently evaluate the relevance of research that is necessary to make decisions regarding drug discovery pipelines.

[0944] Exemplary Government Use Cases

[0945] Analysis of intelligence reports

[0946] Intelligence analysts can leverage the system to summarize lengthy, categorized reports into concise summaries of key findings, patterns, and suggestions.

[0947] Positive zoom provides a broader contextual understanding, bringing key insights to the surface from large volumes of documents.

[0948] Negative zoom can infer connections between disparate reports and incorporate loosely related external data to detect broader trends.

[0949] This will reinforce the analysis of complex geopolitical situations in order to strengthen national security.

[0950] Review of litigation cases

[0951] Government lawyers can use the system to summarize lengthy case files and briefs, and identify core arguments, precedents, evidence, and conclusions.

[0952] Positive zoom focuses on the most relevant and important facts and testimonies in the case.

[0953] Negative zooming can assume different rule implications and incorporate relevant past events and laws as additional context.

[0954] This makes it possible to efficiently prioritize the details of particularly important cases in preparation for hearings and trials.

[0955] Processing of patent applications

[0956] Patent examiners can use the system to summarize lengthy patent applications into a concise overview of the proposed invention, prior art, claims, drawings, and specification.

[0957] Positive zoom extracts key novel features and differentiating factors of the present invention.

[0958] Negative zoom can uncover loosely related patents and incorporate expertise to assess their patentability.

[0959] This makes it possible to quickly understand the essence of complex patent applications and streamline the review process.

[0960] Summary of the bill and amendments

[0961] Policy advisors can use the system to summarize lengthy legislative proposals and amendments into concise summaries.

[0962] Positive zooms focus on key details such as revised sections, secured funding, affected programs, and implementation timelines.

[0963] Negative zooming allows us to infer broader implications and incorporate relevant rules as additional context.

[0964] This provides an efficient understanding of complex legal reforms for advising elected representatives.

[0965] Consideration of grant proposals

[0966] Government grant officials can use the system to summarize lengthy grant applications into abridged summaries that highlight objectives, methods, eligibility, budget, and impact.

[0967] Positive zoom extracts key differentiators between competing proposals.

[0968] Negative zooming allows us to infer potential challenges and incorporate relevant external data to better evaluate proposals.

[0969] This makes it possible to thoroughly and efficiently review many complex proposals in order to determine who will receive the grant.

[0970] Analysis of public comments

[0971] Regulators can use the system to summarize lengthy public feedback received during the rule-making comment period.

[0972] Positive zooming focuses on extracting key themes, suggestions, and concerns from repetitive or redundant comments.

[0973] Negative zoom can infer overall public opinion and incorporate relevant legal background as context.

[0974] This helps to efficiently process large amounts of public input and form effective rules.

[0975] Exemplary Journalism Use Cases

[0976] Live Event Summary

[0977] News media analysts can use the system to generate real-time summaries of unfolding events such as elections, sporting events, and award shows.

[0978] As new information comes in through live blogs, social media, wire feeds, and other sources, the system can ingest and summarize the content in real time.

[0979] Positive zoom identifies important developments, statistics, citations, and highlights as they occur.

[0980] Negative zoom, based on historical data and expertise, can make speculative predictions about possible outcomes as an event progresses.

[0981] Furthermore, contextual information about candidates, players, and nominees can be incorporated to enhance understanding.

[0982] Real-time input and summarization allow the system to output condensed summaries of live events in real time, keeping analysts informed as the event unfolds.

[0983] Low-latency real-time capabilities allow analysts to efficiently monitor many ongoing events simultaneously without becoming overwhelmed by information.

[0984] As soon as new information emerges, key metrics and insights can be extracted, inferred, and quickly responded to and explained.

[0985] Without real-time processing, there will be a lag in capturing input and generating summarized output. This severely limits the usefulness of time-constrained live event monitoring and analysis use cases that require immediate summaries.

[0986] Summary of the raw interview

[0987] Investigative journalists can use the system to summarize long, raw transcripts and audio recordings from interviews with sources into abridged summaries.

[0988] Positive zoom extracts key quotes, facts, and arguments to capture the essence.

[0989] Negative zoom can infer potential bias in a source based on audio patterns and reinforce insights by incorporating relevant investigative reporting data.

[0990] This helps to efficiently extract and condense interview content to inform news articles and documentaries.

[0991] Review of documents

[0992] Reporters can use the system to summarize lengthy documents such as financial reports, court documents, and policy white papers obtained during investigative reporting.

[0993] Positive zoom focuses on highlighting particularly important new facts, figures, and conclusions.

[0994] Negative zoom can create speculative connections to other documents and bring to the surface valuable clues for further investigation based on reporter's notes.

[0995] This allows for the rapid analysis of investigative news materials and the formation of reports.

[0996] Exemplary academic use cases

[0997] Lecture summary

[0998] The system can be used by professors to condense lengthy lectures and textbook chapters into abridged learning materials for students.

[0999] Positive zoom extracts core concepts, examples, and conclusions while removing redundant or peripheral information.

[1000] Negative zoom can help students infer the foundational knowledge they should possess and incorporate illustrative external materials to reinforce explanations.

[1001] This helps in creating lectures and study guides that are tailored, optimized, and condensed to meet the needs of students.

[1002] Analysis of research corpora

[1003] Academic researchers can use the system to analyze large corpora of documents in their field, such as conference proceedings and journal articles.

[1004] Positive zoom helps identify important themes, findings, and gaps in the literature.

[1005] Negative zoom can reveal underlying connections between papers and bring to light promising new scholars.

[1006] This provides an efficient understanding of the overall landscape and trends in that field.

[1007] Other exemplary use cases

[1008] Summary of art history materials

[1009] Art historians can use the system to summarize long articles, books, and documentaries on art history into abridged summaries.

[1010] Positive zoom extracts key details about the artist, movement, influences, techniques, and representative works.

[1011] Negative zoom bridges the gap between different time periods and styles, incorporating details of peripheral context to provide a broader perspective.

[1012] This makes it possible to efficiently examine a large amount of art history resources and identify important themes and trends.

[1013] Music analysis

[1014] Composers can use the system to analyze long scores or recordings of complex pieces to understand the key elements.

[1015] Positive zoom identifies the main melody, harmony, rhythm, instrumentation, song structure, and motifs that define the overall style and sound.

[1016] Negative zoom can infer less obvious patterns in music and reveal deeper insights by incorporating contextual details about composers, periods, and genres.

[1017] This helps composers efficiently grasp the essence of complex compositions and inform their work accordingly.

[1018] Review of architectural documents

[1019] Architects can use the system to summarize long architectural plans, models, and proposals, and extract key details.

[1020] Positive zoom focuses on the spatial layout, design, materials, features, and concepts that shape the architecture.

[1021] Negative zoom can make speculative suggestions for modifications based on building requirements and incorporate external data such as maps and zoning policies as additional context.

[1022] This makes it possible to quickly identify the most important architectural elements from a wide range of documents.

[1023] Analysis of comedy performances

[1024] Aspiring comedians can use the system to summarize video recordings of stand-up specials and improvisational shows.

[1025] Positive zoom identifies key jokes, storytelling techniques, callback patterns, and comedic methods that generate big laughs.

[1026] Negative zoom can infer more subtle humor mechanisms and, by incorporating the comedian's background, reveal deeper insights.

[1027] Summary of AI research paper

[1028] AI researchers can use the system to summarize lengthy academic papers on cutting-edge AI techniques and technological improvements.

[1029] Positive zoom highlights key algorithms, architectures, results, advantages, and limitations of the proposed method.

[1030] Negative zoom can reveal conceptual connections between different papers and bring to the surface upward trends and paradigms in AI research.

[1031] This makes it possible to efficiently review a large number of complex technical papers and keep up-to-date with cutting-edge technologies in rapidly developing fields.

[1032] VR / AR Application Analysis

[1033] VR / AR developers can use the system to analyze extensive documentation and codebases of virtual reality and augmented reality applications.

[1034] Positive zoom extracts core user flows, 3D assets, scene designs, and key software architecture patterns.

[1035] Negative zoom can infer potential challenges when porting to a new platform based on hardware specifications and incorporate contextual competition benchmarking data.

[1036] This helps to efficiently grasp the essence of complex VR / AR projects and assist with maintenance, optimization, and multi-platform support.

[1037] Processing of sensor data

[1038] Engineers can leverage the system to process and summarize real-time data streams from networks of IoT sensors that monitor infrastructure such as factories, bridges, and wind farms.

[1039] Positive zoom helps identify anomalies and particularly important events from noisy sensor readings.

[1040] Negative zoom can enable predictive maintenance by performing predictive fault estimation based on historical trends and external weather data.

[1041] Real-time ingestion and summarization helps optimize infrastructure monitoring by surfacing critical insights from massive sensor data streams.

[1042] Examination of neuroscience research

[1043] Neuroscientists can use the system to summarize lengthy papers on neural networks, brain-computer interfaces, and advanced neuroimaging techniques.

[1044] Positive zoom highlights key findings regarding neurobehavioral, cognitive, and stimulus-response characteristics.

[1045] Negative zoom can identify promising follow-up research directions based on unanswered questions and speculative extrapolation of results.

[1046] This will enable us to maintain our current state in the rapidly developing fields of neuroscience and related technologies.

[1047] This helps new comedians learn efficiently from performances and develop their own comedic skills.

[1048] Summary of legal contract

[1049] Lawyers can use the system to summarize long and complex legal contracts at various levels of detail, from a general, abridged overview to a clause-by-clause breakdown.

[1050] The finest granular positive zoom allows you to extract specific details of terms, conditions, limitations, liability, fees, and other regulations.

[1051] A progressively broader summary moves upward from the sub-section level to summarize contract sections, entire documents, groups of related contracts, and the entire agreement.

[1052] This multi-resolution summarization provides customized abstraction, enabling efficient review of contracts from broad overviews to minute details.

[1053] Source code analysis

[1054] Software engineers can use the system to analyze large codebases by summarizing them at various levels, from a broad overview of the architecture to a granular summary of the logic within the functionality.

[1055] Positive zooming provides hierarchical codebase abstraction, allowing you to extract design patterns, interfaces, modules, classes, and ultimately, line-by-line logic flow.

[1056] Negative zoom can infer dependencies between components that may not be explicit.

[1057] Fine-grained, multi-resolution summaries enable a broad and deep understanding of complex code.

[1058] Summary of health records

[1059] Physicians can leverage highly granular summaries of patients' health records, ranging from abridged medical histories to detailed timelines of symptoms, medications, and interventions.

[1060] The finest granular positive zoom extracts specific details from individual progress notes, test results, and expert reports.

[1061] More broadly, the data is summarized across visits and sources into an integrated timeline and profile of health factors.

[1062] This allows for customized levels of abstraction, enabling efficient analysis of patient records from diverse sources.

[1063] Summary of scientific data

[1064] Researchers can use the system to summarize raw scientific datasets into statistical overviews, data subset reports, visualizations, and granular lists.

[1065] Positive zoom can provide multi-resolution analysis, from general trends to row-level data.

[1066] Negative zoom allows for the analysis of relationships between variables at various levels of granularity.

[1067] Customizable summaries support exploring large, complex data from diverse analytical perspectives.

[1068] Figure 6 is a block diagram illustrating an exemplary training method 600 for the system to optimize its summarization performance over time. As described herein, the system can progress from basic to advanced functions through built-in rules that require minimal manual adjustment. In an exemplary embodiment, this function can be implemented by the learning capability of the content summarization service 120 shown in Figure 1.

[1069] Service 120 initially includes core modules such as the abstraction parameter module 210, the prompt engineering module 220, the language model module 230, and the retention module 240. The system can use these modules or similar modules at a basic level to function, for example, to accept a client summary request, determine abstraction parameters, construct prompts, query the LLM, and / or return a response.

[1070] Over time, additional modules may be added, or deployed modules may be reconfigured (for example, automatically through a machine learning process). For example, an external data module 250, an interaction module 260, an evaluation module 270, and a training module 280 may be added. As these modules are deployed, the system gains more advanced capabilities.

[1071] The external data module 250 allows the system to interface with the external data source 130 to incorporate supplementary information for summarization. For example, it queries a knowledge base to supplement the abstract summary with important facts about the mentioned entities. This module uses techniques such as semantic retrieval and data retrievers to identify external data sources highly relevant to a given summarization task. It may be added after the initial deployment to enhance contextual capabilities.

[1072] The dialogue module 260 leverages its core summarization capabilities to enable the generation of conversational responses via dialogue systems and APIs. For example, taking a summarized description of a customer support case, it generates a natural language response that addresses the key issues detected. The dialogue module enables the progression from static summaries to interactive conversational applications.

[1073] The evaluation module 270 analyzes the summary output using metrics such as semantic equivalence, redundancy, and human validation studies to quantify its quality. By adding this module, the system can begin to autonomously evaluate the performance of different models and prompts to improve accuracy. The metrics help guide the automatic selection of the optimal model and prompt based on performance.

[1074] Training module 280 enables retraining of LLM and other models with new data to improve summarization accuracy over time. This includes sample selection, data preprocessing, hyperparameter tuning, and model optimization. By adding this module, the system can automatically customize models to leverage new training data and increase their relevance to specific summarization tasks and domains.

[1075] These additional modules extend the capabilities of the initial architecture, enabling more advanced summarization, interaction, transparency, evaluation, and training functions. The modular design allows the system to automatically evolve its sophistication over time by deploying new modules.

[1076] Orchestration of multiple LLM & AI modules

[1077] To evolve from basic to complex functionality, the system can leverage and combine multiple (e.g., third-party) LLM and AI modules. The system can employ a microservices approach with each model behind an API, enabling orchestration of model combinations.

[1078] The system can generate multiple candidate summaries using different models for the same prompt. The ensemble models compare the outputs and select the best response based on consensus validation.

[1079] Modular subtasks such as extraction, abstraction, and compression can be assigned to different specialized models. For example, an extraction model might identify key sentences, and then an abstraction model could paraphrase them.

[1080] Models such as classifiers, retrievers, and QA systems can be organized to enhance summarization. For example, a classifier tags topics, and then a retriever identifies relevant facts about those topics.

[1081] The system learns over time, through testing and reinforcement learning, which combination of models generates the best summaries for different use cases.

[1082] These orchestration techniques allow the system to leverage an evolving ensemble of third-party LLM and AI technologies. The models themselves can be maintained externally (e.g., by partners) and continuously improved independently. However, the system evolves by learning to combine and customize models to its specific summarization needs.

[1083] For example, when a new LLM like GPT-4 is released, the system can immediately leverage it through its modular architecture without retraining its internal models. New models are simply added behind the standard API, and the system then learns how to best utilize the new capabilities. This continuous integration of the latest external AI technological improvements fuels the evolution of the system itself.

[1084] training method

[1085] The training method in Figure 6 describes how the system can evolve its summarization performance over time.

[1086] In operation 602, data is generated. For example, the training data includes a large corpus set for pre-training the base model, plus domain-specific document / summary pairs for fine-tuning. In an exemplary embodiment, the data generation module may create a dataset for training.

[1087] For pre-training, diverse documents may be sampled from web crawl data, books, academic papers, news articles, etc., as described herein. This develops the general capabilities of the LLM.

[1088] For fine-tuning, we collect domain-specific corpora such as legal contracts or scientific papers, along with human-written summaries, as discussed in the summaries section. This specializes the model for specific tasks.

[1089] This involves preparing training data using techniques such as cleaning, splitting, and augmentation. The higher the quality and size of the dataset, the better the model performance.

[1090] In operation 604, one or more models are trained.

[1091] In exemplary embodiments, the model training module fine-tunes the LLM and other models using the training dataset, as described herein. The steps may include one or more of the following:

[1092] Choose a model architecture such as BERT or GPT-3. Modular design allows for the integration of new architectures.

[1093] Transfer learning is used to initialize the model with generic pre-trained weights. This leverages advancements in external LLMs.

[1094] Supervised fine-tuning is performed using domain documents to adapt to the target vocabulary, style, and level of abstraction.

[1095] Optimize hyperparameters such as learning rate and dropout via grid search to maximize validation performance.

[1096] The training iterations are repeated until overfitting is minimized and the validation metric plateaus. New data allows for further optimization.

[1097] By continuously training and fine-tuning the model with new summary data, the system can autonomously improve its performance over time.

[1098] In operation 806, an evaluation is performed. For example, the evaluation module may analyze the summary output to derive model improvements as described herein. Techniques may include one or more of the following: Computing automated metrics such as semantic similarity, conciseness, and grammatical accuracy to quantify quality; conducting human evaluation studies for subjective assessments of consistency, precision, and completeness; comparing metrics across model versions to determine performance improvements, with new iterations measured against a baseline; using validation sets to detect overfitting; and model selection based on real-world performance.

[1099] The evaluation metrics allow the system to scientifically track summarization gains over time and select the optimal model.

[1100] In operation 808, one or more models are deployed.

[1101] This operation may involve deploying the top-level running model into the generation and summarization pipeline while archiving past versions. Containerization, as described herein, facilitates smooth model deployment and rollback. Canary testing initially releases the new model to a subset of users. Blue-green deployment seamlessly shifts traffic to the new model once validated. Monitoring continues after deployment to detect potential setbacks.

[1102] By iteratively generating training data, optimizing the model, evaluating the output, and deploying top performers, the system can automatically improve its summarization accuracy over time.

[1103] In exemplary embodiments, a method for optimizing the summarization process across diverse content corpora, including text, audio, video, and image content, is disclosed. This method addresses several technical challenges inherent in the prior art, particularly the inefficiencies associated with processing large amounts of data using large-scale language models (LLMs) or similar techniques. Traditionally, each new level of content summarization requires a complete reprocessing of the original data. This not only consumes considerable computing resources but also results in higher costs, especially when using cloud-based LLM services that charge based on data volume.

[1104] Exemplary optimization

[1105] The techniques described herein include recursive scaling techniques that leverage previously summarized outputs to further abbreviate content. This method significantly reduces the need to iterate through the entire content corpus for each summarization task. For example, a summary reduced to 50% of the original content can be efficiently reduced to 25% by summarizing an already abbreviated version rather than the complete original document. This recursive approach can dramatically reduce associated costs by minimizing computational load and reducing the amount of data processed in each subsequent summarization step. Technical implementation involves sophisticated prompt engineering, where each subsequent prompt is dynamically adjusted based on the output of the preceding summary, thus maintaining consistency and relevance in the progressively abbreviated summary.

[1106] Furthermore, the disclosed techniques can enhance the system's flexibility and / or applicability across various media types. For example, it can process not only text content but also audio, which can be transcribed into text using an advanced speech-to-text model trained to accurately capture the nuances of spoken language. Video content can be processed by extracting keyframes or segments using a video processing algorithm that identifies central moments based on motion, color changes, and / or audio cues, and these keyframes or segments can then be summarized and / or transcribed. Images can be processed using image recognition techniques that describe or analyze visual content to generate text summaries and / or captions. This multi-type capability allows the system to be used in a wide range of applications, from academic research and media production to corporate communications and beyond.

[1107] In addition to addressing data processing inefficiencies, the disclosed technique also introduces a dynamic summarization depth feature. This feature allows the system to provide adapted summaries that can dynamically respond to user needs by adjusting the depth of the summary based on the complexity of the content and / or specific requirements. For example, simpler or shorter content may require only a lower level of summary, while more complex material may benefit from deeper, more detailed processing. This is achieved through an intelligent analysis layer that assesses the complexity of the content and adjusts the summarization parameters accordingly, ensuring that the summary is concise and rich in the necessary details.

[1108] The storage, retrieval, and retrieval of summary content can also be optimized. Summaries are stored in a structured manner and indexed by their level of abstraction, facilitating quick and easy retrieval. This structured storage is implemented using a database management system that supports high-speed queries and data retrieval, allowing users to efficiently access summaries at different levels of detail according to their specific needs. This enhances usability across various scenarios, making the system highly adaptable and user-friendly.

[1109] The disclosed techniques represent a significant technological advancement over prior art, not only by improving the efficiency and / or cost-effectiveness of using LLM for content summarization, but also by enhancing the flexibility and / or user-friendliness of the summarization system. The disclosed techniques stand out as a valuable solution to the technical problems associated with high computational requirements and / or excessive processing costs associated with conventional content summarization techniques, representing a central technological improvement, at least in the fields of data processing and information management.

[1110] In exemplary embodiments, one or more modules carry out the method described herein, which may include one or more of the content summarization applications 120 shown in Figure 1.

[1111] The recursive summarization module can be configured to perform recursive summarization. This module manages the generation of subsequent summaries from preceding summarized content, significantly reducing the need to iterate through the entire original content corpus. It employs one or more advanced algorithms that dynamically adjust prompts to the LLM based on the output of the preceding summaries. This ensures that each subsequent summary maintains consistency and relevance, effectively reducing computational load and associated costs.

[1112] In an exemplary embodiment, the recursive zoom capability enables the creation of a hierarchical structure of summaries at various levels of abstraction. This approach allows the system to efficiently generate summaries of different granularities and navigate between them without iterating through the entire original content.

[1113] In an exemplary embodiment, the system may use the generated preceding summaries as input for further summaries. For example, a summary representing 50% of the original content can be used as a basis for creating a 25% summary, and this 25% summary can then be used to generate a 12.5% ​​summary, and so on. This chaining process creates a tree-like structure of summaries, where each level represents a higher degree of abstraction from the original content.

[1114] The system may employ sophisticated prompt engineering techniques to facilitate this recursive summarization. As each level of summary is generated, the prompt is dynamically adjusted based on the output of the preceding summary. This ensures that subsequent summaries progressively reduce information while maintaining consistency and relevance. The prompts are designed to guide the LLM to preserve important concepts and / or relationships even as the level of abstraction increases.

[1115] In an exemplary embodiment, to implement this hierarchical summarization structure, the system utilizes a combination of extractive and abstractive summarization techniques. At higher levels of the hierarchy (e.g., less abstract), the extractive method becomes more prevalent, allowing for the selection of important sentences or clauses from the original content. As the level of abstraction increases, the system shifts towards more abstract techniques, generating new text that captures the essence of the content in a more condensed form.

[1116] The recursive zoom capability extends to "zooming in" to content, allowing for the expansion of summaries with additional details or external information. This two-way zoom feature provides users with the flexibility to explore content at various levels of detail, seamlessly moving between a general overview and more granular information as needed.

[1117] To optimize performance and resource utilization, the system may implement a caching mechanism for generated summaries. Frequently accessed or anticipated high-demand abstraction levels are prioritized in the cache to ensure rapid retrieval. This caching strategy dynamically adapts based on usage trends and continuously refines its efficiency over time.

[1118] Recursive zooming capabilities not only enhance the efficiency of the summarization process but also provide users with a powerful tool for content exploration and analysis. By navigating the hierarchy of summaries, users can quickly grasp the main ideas of a large corpus of content and then delve into specific areas of interest, making the system highly versatile for a wide range of applications, from academic research to business intelligence.

[1119] The content type processing module is configured to handle a variety of content types, including text, audio, video, and images. This module may contain one or more submodules, each adapted to handle a specific content type. The text processing submodule continuously refines text summarization techniques. The audio processing submodule uses sophisticated speech-to-text models to transcribe audio content into text for summarization. The video processing submodule uses state-of-the-art video processing algorithms to extract keyframes or segments for summarization. The image processing submodule leverages cutting-edge image recognition technology to analyze images and generate descriptive summaries or captions.

[1120] In an exemplary embodiment, the content type processing module implements one or more advanced video summarization techniques that enable efficient processing and / or abstraction of video content at various levels. This enhancement allows the system to handle video data with the same or similar flexibility and depth as text-based content.

[1121] To extract keyframes, the system may employ one or more computer vision algorithms that analyze visual features, motion patterns, and / or scene changes. These algorithms can identify frames representing important moments or transitions within the video and create a visual summary that captures the essence of the content. The density of keyframe extraction can be adjusted based on the desired level of abstraction, with higher levels of abstraction yielding fewer but more representative frames.

[1122] To identify key segments, the system can utilize a combination of audio and visual analysis. For example, speech recognition technology can transcribe spoken content, while natural language processing techniques can identify important topics and / or emotions. Simultaneously, visual analysis can detect scenes with high activity, prominent objects, or faces. By combining these signals, the system can accurately identify the segments that are most relevant or likely to have the greatest impact.

[1123] To generate text summaries of video content, the system can utilize transcribed audio along with metadata such as video title, description, and closed captions. These text elements can be processed using the same or similar summarization techniques applied to purely text-based content, enabling the creation of concise written summaries at different levels of abstraction. Depending on the user's needs, the system can generate anything from short, single-sentence descriptions to more detailed, paragraph-length summaries.

[1124] The video summarization process can be integrated into a recursive zoom framework, allowing users to seamlessly navigate different levels of video abstraction. At its most general level, a user might see a single representative frame with a single sentence of explanation. Zooming in could reveal a sequence of keyframes with short captions, while further zooming could present a longer text summary along with a more comprehensive visual representation.

[1125] To optimize performance, the system can implement intelligent caching of video summaries at various levels of abstraction. Frequently accessed or computationally intensive summaries are prioritized in the cache to ensure rapid retrieval of subsequent requests. This caching strategy dynamically adapts based on usage trends and content popularity, constantly refining its efficiency over time. The dynamic summarization depth module is configured to implement dynamic summarization depth features, which adjust the depth of the summary based on the complexity of the content or specific requirements. This integrates an intelligent analysis layer that assesses the complexity of the content and adapts the summarization parameters accordingly. This module ensures that the generated summaries are concise and rich in necessary details, enhancing the system's suitability to user needs.

[1126] The summary storage and retrieval module is designed to optimize the storage, retrieval, and retrieval of summary content. It organizes summaries in a structured manner, indexing them by their level of abstraction. This module utilizes an advanced database management system that supports high-speed queries and efficient data retrieval, enabling users to quickly and easily access summaries at different levels of detail.

[1127] In an exemplary embodiment, the summary storage and retrieval / retrieval module includes a sophisticated vector-space indexing system that can significantly improve the efficiency and flexibility of content retrieval / retrieval. This approach may leverage advanced natural language processing techniques to create a semantic index of the summary content.

[1128] In an exemplary embodiment, the system converts raw text into a high-dimensional vector representation to capture the semantic essence of the content. These vectors are then used to create a semantic index, which can enable efficient searching and retrieval of semantically similar content and / or their corresponding nested summaries at various zoom levels. This vector space approach allows the system to find cached nearby semantic content, which can be used to inform or accelerate the summarization process of new, relevant content.

[1129] Unlike conventional indexing methods, this vector-space indexing system can accommodate a continuous range of abstraction levels. This flexibility allows the system to interpolate between existing summary levels to accurately deliver content at the requested level of detail, enabling more granular searching and retrieval of summaries. Furthermore, the vector-space model can facilitate the implementation of advanced similarity search algorithms, enabling rapid identification of relevant content across the entire corpus.

[1130] A vector space indexing system can also enhance the recursive zooming capability of the summarization process. As summaries are generated at different levels of abstraction, their vector representations can be stored in an index, creating a hierarchical structure of semantic embeddings. This hierarchy allows for efficient movement between different levels of abstraction and supports both zoom-out (e.g., contraction) and zoom-in (e.g., expansion with external data) operations.

[1131] To optimize performance and resource utilization, the system may employ a sophisticated caching mechanism that leverages semantic relationships captured within a vector space. Frequently accessed or semantically central summaries are prioritized in the cache, ensuring the rapid retrieval and acquisition of commonly requested information. This caching strategy can dynamically adapt based on usage trends and / or semantic relevance, continuously refining its efficiency over time.

[1132] This vector space indexing approach can not only enhance the search and retrieval speed and / or flexibility of the summarization system, but also provide the foundation for advanced analytical and content discovery features. By analyzing the distribution and / or relationships of vectors in semantic space, the system can identify trends, clusters, and / or outliers in the summarized content, providing valuable insights to users and / or further enhancing the usefulness of the summarization platform.

[1133] The cost-efficiency analysis module is designed to calculate the cost savings achieved by the recursive summarization method compared to the traditional method where the entire content corpus is reprocessed for each summary. This module helps quantify the economic benefits of the new summarization strategy and provides a valuable perspective on the system's cost-effectiveness.

[1134] Integration with real-time systems This technology can be extended to integrate with real-time data streams, which can be useful for applications requiring immediate content processing, such as media monitoring or live event summarization. This integration involves optimizing the system to efficiently handle and process continuous input streams, ensuring that summarization is performed in real time without significant delays. Techniques such as stream processing and event-driven programming can be used to effectively manage these data flows, enabling the system to provide timely summaries, which are particularly important for decision-making processes in dynamic environments.

[1135] Advanced security measures Given that it may contain potentially sensitive multimedia content, strengthening the system's security measures may be crucial. This involves implementing robust encryption methods for data in motion and at rest, along with strict access controls to ensure that only authorized users can access the summarization function. Furthermore, compliance with international data protection regulations such as GDPR and HIPAA will be addressed to ensure that the system meets global security standards. This will not only protect user data but also build confidence in the system's ability to handle sensitive information securely.

[1136] Machine learning optimization techniques To further enhance the efficiency and accuracy of the summarization process, the system can incorporate advanced machine learning optimization techniques. This includes the use of transfer learning to quickly adapt pre-trained models to specific content types or domains, and active learning strategies in which the model learns from user feedback and continuously improves its summarization output. These techniques ensure that the system remains adaptable and can provide high-quality summaries that meet the requirements of specific users.

[1137] User interaction and customization features The system can also enhance user interaction by developing a customizable user interface that allows users to specify preferences for summaries, such as the depth of detail, content type, and presentation style. Furthermore, a feedback loop may be provided that allows users to evaluate summaries and / or provide suggestions for improvement, which helps refine the algorithm and ensure that the output aligns with user expectations. This level of customization and interaction can not only improve user satisfaction but also enhance the system's usefulness across different use cases.

[1138] Multilingual and cross-linguistic proficiency The system can be configured to handle multiple languages, for example, to enhance its applicability in global markets. This involves training models on diverse language datasets and / or integrating automated language detection to adapt the summarization process to the language of the content. Supporting multilingual content processing ensures that the system can serve a wider audience and handle international data more effectively.

[1139] Environmental and operational cost analysis A detailed analysis of the environmental impact and operating costs associated with system deployment can be generated. This includes assessing the energy consumption of the data centers where the system may be deployed and exploring ways to optimize the infrastructure to reduce its carbon footprint. Furthermore, a cost-benefit analysis that considers not only computational savings but also potential reductions in the efficiency of human effort and time can be detailed to provide a comprehensive overview of the system's economic and environmental impacts.

[1140] In exemplary embodiments, the indexing system offers exceptional flexibility, accommodating a wide range of abstraction levels beyond fixed zoom increments. This advanced approach provides a more granular and semantic-driven indexing capability than that offered by conventional systems.

[1141] In an exemplary embodiment, rather than relying on a predetermined zoom level, the system utilizes a continuous level of abstraction to enable accurate retrieval of summaries at any desired level of detail. In an exemplary embodiment, this can be achieved through a sophisticated semantic similarity model underpinning the indexing process. By transforming content into a high-dimensional vector representation, the system can measure the semantic distance between different content and their summaries, enabling a more granular, context-aware approach to content retrieval.

[1142] The flexibility of this indexing system extends to its ability to interpolate between existing summaries. For example, when a user requests a summary at a specific level of abstraction that does not directly correspond to a pre-calculated summary, the system can dynamically generate an appropriate summary by intelligently combining and refining cached nearby summaries. This ability ensures that users can access content precisely at the level of detail they need, without being constrained by predefined zoom increments.

[1143] Furthermore, indexing based on semantic similarity facilitates more efficient and effective content discovery. Users can explore related content based on semantic proximity rather than relying solely on explicit hierarchical relationships. This approach allows systems to surface semantically related information that might not be apparent through traditional indexing methods, enabling unexpected discoveries and more intuitive navigation of large content repositories.

[1144] The system's indexing flexibility also enhances its ability to consistently handle diverse content types. Whether dealing with text, audio transcripts, video metadata, or image descriptions, the semantic indexing approach provides a unified framework for organizing, searching, and retrieving content across different media formats. This cross-media capability ensures users can seamlessly navigate between different content types while maintaining consistent abstraction control.

[1145] By implementing this highly flexible, semantic-driven indexing system, the system provides a more adaptive and user-centric summarization platform. It can address a wide range of user needs, from broad overviews to highly specific and detailed summaries, all within a unified, intuitive interface. This approach not only enhances the user experience but also improves the overall efficiency and effectiveness of content exploration and analysis across various domains and applications.

[1146] The system's efficiency is significantly enhanced through the technically improved use of cached, nearby semantic content, leveraging semantically similar content to streamline the summarization process for new, relevant content. This approach maximizes the semantic relationships between different content, enabling faster and more accurate summarization.

[1147] In an exemplary embodiment, when processing new content, the system first analyzes its semantic structure and compares it to an existing cache of summarized content. By identifying semantically similar content that has already been summarized, the system can use these cached summaries as a starting point or template for generating new summaries. This method reduces the computational load and time required for summarization because the system does not need to start from scratch for each piece of content.

[1148] Cached nearby semantic content can be particularly useful in scenarios where content shares similar themes, structures, or domain-specific languages. For example, in legal documents, contracts with similar clauses or scientific papers in related fields can benefit from this approach. The system can quickly adapt existing summaries of semantically similar content, adjusting for specific differences in new content while maintaining the overall structure and likely key points of relevance.

[1149] Furthermore, the system can employ a dynamic learning mechanism that continuously refines its understanding of semantic relationships based on user interaction and feedback. As more content is processed and summarized, the system becomes increasingly adept at identifying relevant cached semantics and effectively applying them to new content. This adaptive approach ensures that the efficiency gains from leveraging cached nearby semantics improve over time, leading to a progressively faster and more accurate summarization process.

[1150] The use of cached nearby semantics can also be extended to recursive zoom functionality. When generating summaries at different levels of abstraction, the system can retrieve and use cached summaries of semantically similar content at the corresponding levels of abstraction. This enables the rapid generation of multi-level summarization hierarchies, allowing users to seamlessly navigate between different levels of detail with minimal processing latency.

[1151] By incorporating this sophisticated approach into leveraging cached, nearby semantic content, the system not only improves its operational efficiency but also enhances the quality and consistency of its summaries across relevant content. This feature represents a significant advance in content summarization technology, enabling faster, more accurate, and / or more contextually relevant summaries across diverse domains and content types.

[1152] In an exemplary embodiment, the method comprises one or more operations, the operations of receiving a content corpus to be summarized; generating a first summarized content by applying a Large Language Model (LLM) or appropriate processing model to the content corpus to create an initial summary at a first predefined level of abstraction; recursively generating subsequent summarized content by progressively applying the Large Language Model or appropriate processing model to the preceding summarized content at higher levels of abstraction, each subsequent summary being derived from the final summarized content without reprocessing the content corpus; and / or storing each of the subsequent summarized content at multiple levels of abstraction to facilitate efficient retrieval, retrieval, and use based on user-defined summarization needs.

[1153] In an exemplary embodiment, the content corpus includes one or more of the following: text, audio, video, and image content. In an exemplary embodiment, recursively generating subsequent summary content utilizes the preceding summary output to perform further summarization. In an exemplary embodiment, multiple levels of abstraction for generating subsequent summary content include multiple specific percentages to support a stepwise approach to content reduction. In an exemplary embodiment, the method further includes calculating the cost savings of recursively generating subsequent summary content compared to a technique in which the content corpus is reprocessed for each summary. In an exemplary embodiment, the LLM or appropriate processing model is configured to dynamically adjust the depth of summarization based on the complexity or length of the content corpus. In an exemplary embodiment, storing each of the subsequent summary contents includes indexing each of the subsequent summary contents based on its level of abstraction to increase the speed of search and retrieve in one or more applications that require different levels of content detail.

[1154] Exemplary mobile device

[1155] Figure 7 is a block diagram illustrating a mobile device 1000 according to an exemplary embodiment. The mobile device 1000 may include a processor 1602. The processor 1602 can be any of several different types of commercially available processors suitable for the mobile device 1000 (e.g., an XScale architecture microprocessor, a MIPS (Microprocessor without Interlocked Pipeline Stages) architecture processor, or another type of processor). Memory 1604, such as random access memory (RAM), flash memory, or other types of memory, is typically accessible to the processor 1602. Memory 1604 may be adapted to store an operating system (OS) 1606 as well as an application program 1608, such as a mobile location-aware application that can provide location-based services (LBS) to the user. The processor 1602 can be coupled directly or via appropriate intermediate hardware to a display 1610 and one or more input / output (IO) devices 1612, such as a keypad, touch panel sensor, or microphone. Similarly, in some embodiments, the processor 1602 may be coupled to a transceiver 1614 that interfaces with the antenna 1616. Depending on the nature of the mobile device 1000, the transceiver 1614 may be configured to both transmit and receive cellular network signals, radio data signals, or other types of signals via the antenna 1616. Furthermore, in some configurations, a GPS receiver 1618 may also utilize the antenna 1616 to receive GPS signals. Modules, Components, and Logic

[1156] Certain embodiments described herein include logic or a set of components, modules, or mechanisms. A module may constitute either a software module (e.g., code embodied (1) on a non-temporary machine-readable medium, or (2) in a transmitted signal) or a hardware implementation module. A hardware implementation module is a tangible unit capable of performing a particular operation and may be configured or arranged in a particular manner. In various exemplary embodiments, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware implementation module that operates to perform a particular operation as described herein.

[1157] In various embodiments, hardware implementation modules may be mechanically or electronically implemented. For example, a hardware implementation module may include dedicated circuitry or logic permanently configured to perform a specific operation (e.g., as a dedicated processor such as a field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC)). A hardware implementation module may also include programmable logic or circuitry temporarily configured by software to perform a specific operation (e.g., contained within a general-purpose processor or other programmable processor). It will be understood that the decision of whether to implement a hardware implementation module mechanically, with dedicated, permanently configured circuitry, or with temporarily configured circuitry (e.g., configured by software) may depend on cost and time considerations.

[1158] Therefore, the term “hardware implementation module” should be understood to encompass a tangible entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a particular manner and / or to perform specific operations described herein. Considering embodiments in which a hardware implementation module is temporarily configured (e.g., programmed), each hardware implementation module does not need to be configured or instantiated at any given point in time. For example, if a hardware implementation module includes a general-purpose processor configured using software, the general-purpose processor may be configured as different hardware implementation modules at different times. The software may, therefore, configure the processor to configure a particular hardware implementation module at one point in time and different hardware implementation modules at different points in time.

[1159] Hardware implementation modules can provide information to other hardware implementation modules and receive information from other hardware implementation modules. Therefore, the described hardware implementation modules can be considered to be communicatively coupled. When multiple such hardware implementation modules exist simultaneously, communication can be achieved through signal transmissions connecting the hardware implementation modules (e.g., via appropriate circuits and buses). In embodiments where multiple hardware implementation modules are configured or instantiated at different times, communication between such hardware implementation modules can be achieved, for example, through the storage and retrieval / retrieval of information in a memory structure accessible to the multiple hardware implementation modules. For example, one hardware implementation module may perform an operation and store the output of that operation in a communicatively coupled memory device. Further hardware implementation modules can then access the memory device at a later time to retrieve, retrieve, and process the stored output. Hardware implementation modules may also initiate communication with input or output devices and operate on resources (e.g., sets of information).

[1160] Various operations of the exemplary methods described herein may be performed, at least partially, by one or more processors that are configured (e.g., by software) temporarily or permanently to perform the operations in question. Whether configured temporarily or permanently, such processors may constitute a processor implementation module that operates to perform one or more operations or functions. The modules referred to herein may include processor implementation modules in some exemplary embodiments.

[1161] Similarly, the methods described herein can be at least partially processor-implemented. For example, at least some of the operations of the methods may be performed by one or more processors or processor implementation modules. Some executions of the operations may reside not only within a single machine but also distributed among one or more processors deployed across several machines. In some exemplary embodiments, the processor(s) may be located in a single location (e.g., in a home environment, an office environment, or as a server farm), while in other embodiments, the processors may be distributed across several locations.

[1162] One or more processors may also operate in a “cloud computing” environment or as “software as a service” (SaaS) to support the execution of related operations. For example, at least part of the operations may be performed by a group of computers (as an example of machines containing processors), and these operations may be accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application programming interfaces (APIs)). Electronic devices and systems

[1163] Exemplary embodiments may be implemented in digital electronic circuits, or in computer hardware, firmware, software, or a combination thereof. Exemplary embodiments may be implemented using computer program products, such as data processing devices, such as programmable processors, computers, or multiple computers, for execution or control thereof, using information carriers, such as computer program products tangibly embodied in machine-readable media.

[1164] Computer programs can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as standalone programs or as modules, subroutines, or other units suitable for use in a computing environment. Computer programs can be deployed to run on a single computer, on multiple computers at a single site, or on multiple computers distributed across multiple sites and interconnected by a communication network.

[1165] In exemplary embodiments, the operation may be performed by one or more programmable processors that execute a computer program to perform a function by acting on input data and generating an output. The operation of the method in exemplary embodiments may also be performed by dedicated logic circuits, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), and the device may be implemented as such dedicated logic circuits.

[1166] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact via a communication network. The client-server relationship arises from computer programs running on each computer that have a client-server relationship with each other. In embodiments of deploying a programmable computing system, it will be understood that both hardware and software architectures are worth considering. Specifically, it will be understood that the choice of whether to implement certain functions in persistently configured hardware (e.g., ASICs), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or in a combination of persistently and temporarily configured hardware, may be a design choice. The following describes various exemplary hardware (e.g., machine) and software architectures that may be deployed in different exemplary embodiments.

[1167] Exemplary machine architectures and machine-readable media

[1168] Figure 8 is a block diagram of an exemplary computer system 1100 in which the techniques and operations described herein can be performed according to an exemplary embodiment. In alternative embodiments, the machine may operate as a standalone device or be connected to other machines (e.g., networked). In a networked deployment, the machine may operate as a server or client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, web appliance, network router, switch or bridge, or any machine capable of executing instructions (sequentially or in other ways) that specify actions to be performed by that machine. Furthermore, although only a single machine is illustrated, the term “machine” also includes any set of machines that individually or collectively execute a set (or set) of instructions to perform any one or more of the techniques described herein.

[1169] An exemplary computer system 1100 includes a processor 1702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), main memory 1704, and static memory 1706, which communicate with each other via a bus 1708. The computer system 1100 may further include a graphics display unit 1710 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 1100 also includes an alphanumeric input device 1712 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 1714 (e.g., a mouse), a storage unit 1716, a signal generation device 1718 (e.g., a speaker), and a network interface device 1720. Machine-readable media

[1170] The storage unit 1716 includes a machine-readable medium 1722 in which one or more sets of instructions and data structures (e.g., software) 1724 that embody or utilize any one or more of the methods, operations, or functions described herein are stored. The instructions 1724 may also reside entirely or at least partially in the main memory 1704 and / or the processor 1702 during their execution by the computer system 1100, and the main memory 1704 and the processor 1702 also constitute the machine-readable medium.

[1171] Although the machine-readable medium 1722 is shown as a single medium in exemplary embodiments, the term “machine-readable medium” may include a single or multiple mediums (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more instructions 1724 or data structures. The term “machine-readable medium” shall also be interpreted as including any tangible medium capable of storing, encoding, or carrying instructions for machine execution (e.g., instructions 1724), causing a machine to implement any one or more of the methodologies of this disclosure, or storing, encoding, or carrying data structures that are utilized by or associated with such instructions. Accordingly, the term “machine-readable medium” shall be interpreted as including, but not limited to, solid memory, as well as optical and magnetic media. Specific examples of machine-readable media include, for example, semiconductor memory devices such as EPROM (Electrically Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and non-volatile memory including CD-ROMs and DVD-ROM disks.

[1172] Instruction 1724 may be further transmitted or received via a communication network 1726 using a transmission medium. Instruction 1724 may be transmitted using a network interface device 1720 and one of several well-known transmission protocols (e.g., HTTP). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet, cellular networks, POTS (Plain Old Telephone Service) networks, and wireless data networks (e.g., WiFi® and WiMAX networks). The term "transmission medium" includes any intangible medium capable of storing, encoding, or carrying instructions for execution by a machine, including digital or analog communication signals, or other intangible mediums for facilitating the communication of such software.

[1173] While embodiments are described with reference to specific exemplary embodiments, it will be apparent that various modifications and changes can be made to these embodiments without departing from the broader scope of this disclosure. Therefore, this specification and the drawings should be considered illustrative, not restrictive. The accompanying drawings, forming part of this specification, illustrate, not restrictive, specific embodiments in which the subject matter may be carried out. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to carry out the teachings disclosed herein. Other embodiments may be derived from the utilized and illustrated embodiments so as to be structural and logical substitutions and modifications without departing from the scope of this disclosure. Therefore, this detailed description should not be construed as restrictive, and the scope of the various embodiments is defined only by the accompanying claims, along with the entire scope of equivalents to which such claims are entitled. While specific embodiments have been illustrated and described herein, it should be understood that any configuration calculated to achieve the same objective may be supplemented in place of the specific embodiments shown. This disclosure is intended to encompass any and all adaptations or variations of the various embodiments. Combinations of the above embodiments, as well as other embodiments not specifically described herein, will become apparent to those skilled in the art upon consideration of the above description.

Claims

1. It is a system, One or more computer implementation subsystems, wherein the one or more computer implementation subsystems are configured to perform an operation, and the operation is Identifying a first content item, wherein the first content item includes a set of sub-content items. Determining the level of abstraction for the aforementioned content items, The method involves automatically generating prompts for provision to a Large-Scale Language Model (LLM), wherein the prompts include a reference to the first content item and the level of abstraction of the first content item. Receiving a response from the LLM to the prompt, wherein the response includes a second content item, the second content item includes a representation of the first content item generated by the LLM, the representation omitting or simplifying one or more of the set of sub-content items based on the level of abstraction, A system including controlling the output communicated to a target device using the aforementioned expression.

2. The system according to claim 1, wherein the level of abstraction is specified as a percentage of the scale of the content item to be used by the LLM to limit the corresponding scale of the representation.

3. The system according to claim 2, wherein the scale relating to the content item relates to the length or size of the content item, and the corresponding scale of the representation relates to the length or size of the representation.

4. The system according to claim 1, wherein determining the level of abstraction is based on input received from a user via a graphical user interface or an additional graphical user interface.

5. The system according to claim 1, further comprising: requesting the LLM to perform one or more inferences based on the content item, and generating information about the one or more inferences to be included in the representation, based on the abstraction level exceeding a configurable threshold;

6. The system according to claim 5, further comprising constructing the prompt to require the LLM to generate the information relating to the one or more inferences using one or more external data sources.

7. The system according to claim 1, wherein the use of the expression includes creating an additional prompt for the LLM, the additional prompt requiring the LLM to generate an appropriate conversational response to the expression based on the context associated with the expression and the entity represented by the system.

8. The system according to claim 7, wherein the entity is a sales representative, and the context includes information relating to sales calls handled in real time by the sales representative.

9. The system according to claim 8, wherein the sub-content items include one or more statements made by the customer and one or more statements made by the sales representative.

10. The system according to claim 9, wherein the sales representative is an intelligent agent.

11. Identifying a first content item, wherein the first content item includes a set of sub-content items. Determining the level of abstraction for the aforementioned content items, The method involves automatically generating prompts for provision to a Large-Scale Language Model (LLM), wherein the prompts include a reference to the first content item and the level of abstraction of the first content item. Receiving a response from the LLM to the prompt, wherein the response includes a second content item, the second content item includes a representation of the first content item generated by the LLM, the representation omitting or simplifying one or more of the set of sub-content items based on the level of abstraction, A method comprising controlling the output communicated to a target device using the aforementioned expression.

12. The method according to claim 11, further comprising: requesting the LLM to make one or more inferences based on the content item, and generating information about the one or more inferences to be included in the representation, based on the abstraction level exceeding a configurable threshold;

13. The method of claim 12, further comprising constructing the prompt to require the LLM to generate the information relating to the one or more inferences using one or more external data sources.

14. The method according to claim 11, wherein the use of the expression includes creating an additional prompt for the LLM, the additional prompt requiring the LLM to generate an appropriate conversational response to the expression based on the context associated with the expression and the entities represented by the system.

15. The method according to claim 14, wherein the entity is a sales representative, and the context includes information relating to sales calls handled in real time by the sales representative.

16. A tangible computer-readable storage medium that stores a set of instructions that cause one or more computer processors to perform an operation when executed by said computer processors, wherein the operation is: Identifying a first content item, wherein the first content item includes a set of sub-content items. Determining the level of abstraction for the aforementioned content items, The method involves automatically generating prompts for provision to a Large-Scale Language Model (LLM), wherein the prompts include a reference to the first content item and the level of abstraction of the first content item. Receiving a response from the LLM to the prompt, wherein the response includes a second content item, the second content item includes a representation of the first content item generated by the LLM, the representation omitting or simplifying one or more of the set of sub-content items based on the level of abstraction, A tangible computer-readable medium, including controlling the output communicated to a target device using the aforementioned expression.

17. The operation further comprises constructing the prompt to request the LLM to perform one or more inferences based on the content item, and to generate information about the one or more inferences to be included in the representation, based on the abstraction level exceeding a configurable threshold, according to claim 16.

18. The operation further comprises constructing the prompt to require the LLM to generate the information relating to the one or more inferences using one or more external data sources, according to claim 17, a tangible computer-readable medium.

19. The use of the expression comprises creating an additional prompt for the LLM, the additional prompt requiring the LLM to generate an appropriate conversational response to the expression based on the context associated with the expression and the entities represented by the system, according to claim 16.

20. The tangible computer-readable medium according to claim 19, wherein the entity is a sales representative, and the context includes information relating to sales calls handled in real time by the sales representative.

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