Natural Language Artifact Recombination for Coherent Articles
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Solution Overview
Problem
Existing NLP technologies struggle to combine diverse outputs from multiple pipelines into a cohesive article and lack personalization for user preferences, resulting in unrelated information and inability to generate tailored content.
Innovation Solution
An NLP pipeline using a Multiple Knapsack Problem (MKP) algorithm and transformer models optimizes the combination of inputs from multiple pipelines, incorporating user preferences through sentiment analysis and masking techniques to generate personalized machine-authored content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple NLP pipelines are used to extract different forms of information (statistical information and editorial commentary), then the diversity of information types is improved, but the ability to combine outputs into a cohesive summary deteriorates
Solution Approach 1:
The patent introduces an intermediary NLP pipeline that acts as a mediator between multiple specialized NLP pipelines and the final summarization system. This intermediary pipeline receives diverse inputs (statistical information, editorial commentary, and other unstructured data), processes them through unified natural language generation techniques, and outputs coherent summaries. The intermediary transforms heterogeneous data formats into a standardized structure that can be seamlessly integrated, resolving the contradiction between information diversity and combinability.
2Productivity
If standard NLP pipelines are used to generate content, then the processing speed is improved, but the personalization capability for user preferences deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the encoder-decoder model on diverse datasets that include various writing styles, tones, and content preferences. This pre-training establishes a foundation of user preference patterns that the model can quickly adapt to during inference. The model is preliminarily equipped with the capability to generate personalized content without sacrificing generation speed, as the personalization occurs through efficient attention mechanisms rather than slow retraining processes.
Solution Approach 2:
The patent implements dynamics through the use of attention mechanisms in the transformer model that dynamically adjust to user preferences during content generation. The model can adapt its processing in real-time based on identified user preferences, dynamically weighting different input features and generating content that aligns with user preferences while maintaining high generation speed through parallel processing capabilities of the transformer architecture.
3Measurement precision
If NLP pipelines focus on specific aspects of a topic, then the depth of information on that aspect is improved, but the coherence when combining with other aspects deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the content generation process into distinct modules: an encoder that processes diverse input data segments (statistical information, editorial commentary, etc.), a transformer model that integrates these segments through attention mechanisms, and a decoder that generates coherent output. Each specialized NLP pipeline processes specific data segments independently, maintaining depth of information, while the transformer model ensures coherence by dynamically weighting and integrating these segments based on their relevance to the overall summary topic.
Data Source
AI summary
An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.