Instant Messaging Transcript Summarization via Segmentation
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Solution Overview
Problem
Users joining instant messaging conversations often miss important discussions due to messages scrolling off the screen, and existing summarization technologies do not effectively provide summaries of previous conversations to new participants or those who have been distracted.
Innovation Solution
A method for summarizing instant messaging transcripts by selecting relevant text based on properties of the document, using a summarization engine to process and segment the chat data, and presenting a summary to new participants or those who have been distracted, with varying levels of summarization for different segments to prioritize recent and relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If chat transcripts are displayed in real-time, then users can follow the conversation flow, but messages scroll off the screen and become lost when users are distracted or join late
Solution Approach 1:
The patent extracts and summarizes only the essential information from chat transcripts, separating key content from the full message stream. This allows users to access critical chat history without being overwhelmed by the complete transcript, effectively preventing information loss while maintaining system simplicity.
Solution Approach 2:
The system performs summarization in advance, creating condensed versions of chat histories before users need them. This preliminary processing ensures that when users join late or return after being distracted, the summarized content is already prepared and immediately available, preventing information loss without requiring complex real-time processing.
2Loss of information
If complete chat transcripts are provided to new participants, then they can fully understand previous discussions, but the volume of information becomes overwhelming and difficult to process
Solution Approach 1:
The patent segments chat transcripts into meaningful units and generates summaries for each segment. This segmentation allows new participants to understand previous discussions in manageable portions rather than facing the complete transcript at once, reducing the time needed to process information while maintaining completeness of context.
Solution Approach 2:
The system changes the parameter of information density by creating summarized versions with varying levels of detail. This allows users to quickly grasp essential context without reading every message, significantly reducing the time required to understand chat history while preserving the completeness of conversation context through strategic selection of key information.
3Productivity
If automated summarization is implemented, then users can quickly catch up on missed conversations, but the summarization technology becomes complex and resource-intensive
Solution Approach 1:
The patent implements self-service summarization where the system automatically generates and updates summaries without requiring user intervention. This automated approach enables users to quickly catch up on missed conversations by simply accessing the pre-generated summaries, dramatically improving productivity while the system handles the complexity of the summarization engine internally.
4Loss of information
If all chat messages are retained and displayed, then no information is lost, but the interface becomes cluttered and important information is difficult to locate
Solution Approach 1:
The patent extracts and highlights only the most important information from chat transcripts, separating key content from the full message stream. This extraction maintains retention of all chat details in the background while presenting only essential information in the foreground, making it easy for users to locate key information without interface clutter.
Solution Approach 2:
The system applies local quality by providing different levels of information presentation in different interface areas. Summarized content with high information density is presented in compact forms, while full details remain accessible on demand. This local differentiation allows users to easily find key information through the summarized view while preserving access to complete chat details when needed.
Data Source
AI summary
Summarization of text in a document may be requested in dependence upon the position of the text in relation to other text within the document or the position of the document containing the text within a plurality of documents in a document structure. Summarization of text in a document may also be requested in dependence upon a user's interaction with an application in conjunction with a version of the document or with a document structure including the document. Different levels of summarization may be applied to different segments of text within a document.