Unified Insights Engine for Multi-Source Media Abstract Generation
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Solution Overview
Problem
Conventional generative pre-trained transformers (GPTs) and abstractive text summarizers struggle to generate summaries of current events and consolidate information from multiple sources, lacking transparent interpretability and being limited to processing text data.
Innovation Solution
A system and method utilizing a language model service trained on multiple data sources to generate media abstracts from a plurality of sources. The system includes a server with a data collection service that retrieves data from various media sources and a language model service that processes this data to create concise summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional GPTs are used to generate text summaries, then natural language output can be produced, but the system cannot provide summaries of current events occurring after training
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing media data from multiple sources in advance. This pre-collected data is then available for generating summaries of current events, allowing the system to respond to queries about recent events without requiring retraining of the language model.
Solution Approach 2:
An intermediary component (data collection and retrieval system) is introduced between the language model and current events. This intermediary continuously gathers media data and makes it available to the language model, enabling the model to summarize current events without needing to be retrained on new information.
2Loss of information
If conventional abstractive text summarizers are used, then text processing can be performed, but the system cannot consolidate information from a plurality of sources effectively
Solution Approach 1:
The system merges multiple data sources by collecting media data from various sources into a unified data store. This consolidated data is then processed by the language model to generate unified summaries that integrate information from all sources, effectively solving the consolidation problem.
Solution Approach 2:
The language model service is designed with multi-functionality to handle diverse data types from multiple sources. It can process and consolidate information from different media sources while maintaining a single unified interface for generating summaries, reducing the need for source-specific processing logic.
3Loss of information
If users access multiple media sources directly, then complete information can be obtained, but the demand on client devices, servers, and network increases significantly
Solution Approach 1:
The system extracts only the essential and relevant information from multiple media sources, consolidating it into concise summaries. This extraction approach delivers complete essential information while significantly reducing the data volume that needs to be transmitted and processed, thereby reducing energy consumption on client devices, servers, and the network.
4Loss of information
If conventional summarizers are used, then text processing can be performed, but transparent interpretability of how output was generated is not provided
Solution Approach 1:
The system incorporates feedback mechanisms that provide transparent interpretability by showing users which source materials contributed to generated summaries. This feedback loop allows users to trace back the origin of summary content, understanding how the output was generated without requiring complex additional infrastructure.
Data Source
AI summary
Systems and methods disclosed herein relate generally to providing a media abstract for a selected topic. The media abstract may include information obtained from a plurality of media sources. Providing the media abstract may include periodically retrieving data from a plurality of media sources, saving the data in a data store, retrieving the data from the data store, generating for each topic of a plurality of topics, a media abstract that includes a plurality of text segments transforming the data, receiving a selected topic from a client device, and transmitting the media abstract corresponding to the selected topic to the client device.