Contextual Summarization for Online Chat Channels
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Solution Overview
Problem
Existing online chat systems are inefficient in organizing and summarizing voluminous conversation data, requiring users to spend significant time manually reviewing scattered information, even with search functions or hashtags.
Innovation Solution
A contextual conversation summarization system that tracks real-time natural language posts, identifies user identities and entities, and generates personalized summaries using neural networks, visualizing conversation data in a structured format for easier review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually review conversation texts, then they can access detailed information, but it requires significant time and effort
Solution Approach 1:
The system extracts key information from scattered conversation texts by identifying entities, actions, and sentiments. It pulls out essential elements such as user identities, key actions, and important entities, organizing them into structured summaries that retain critical information while eliminating redundant content.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw conversation data and user review. This layer includes modules for entity recognition, action identification, and sentiment analysis that mediate the transformation of unstructured text into organized summaries, reducing the time users need to spend reviewing while preserving essential information.
2Adaptability or versatility
If conversation data is organized by hashtags, then users can access topic-specific content, but information remains scattered and disorganized
Solution Approach 1:
The system segments conversation data into distinct structured components including entity information, action items, sentiment analysis, and key events. By dividing the unstructured text into organized segments with specific categories and attributes, it maintains topic accessibility while presenting information in a systematic, easy-to-navigate format.
Solution Approach 2:
The patent transforms one-dimensional hashtag-based topic organization into multi-dimensional structured data organization. It adds dimensions such as entity types, action categories, sentiment polarity, and temporal relationships, creating a rich structured framework that preserves topic accessibility while significantly improving information organization and retrievability.
3Difficulty of detecting and measuring
If search functions are used to find relevant conversation lines, then users can locate specific topics, but information is still scattered and requires manual review
Solution Approach 1:
The system performs preliminary organization of conversation data into structured formats before users need to review it. By pre-processing conversations to extract entities, actions, and sentiments into organized summaries, it eliminates the need for users to manually search and consolidate information, directly addressing both location efficiency and information consolidation needs.
Data Source
AI summary
Embodiments described herein provide a contextual conversation summarization mechanism on a chat platform. Specifically, a communication interface receives a first conversation line originated from a first user and a second conversation line originated from a second user. The mechanism then determines that the first conversational line and the second conversational line belong to a same conversation channel based on a channel indicator. The first conversation line and the second conversation line are concatenated as an input to a summarization model. The summarization model then generates a summary of the conversation channel associated with a timestamp. A first visualization element representing a first conversation contribution by the first user and a second visualization element representing a second conversation contribution by the second user are generated based on the generated summary. A user interface visualization of the summary is generated using the first visualization element and the second visualization element.