Multi-Modal Summarization for Scattered Chat Data
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Solution Overview
Problem
Existing online chat systems are inefficient for users to obtain information from voluminous and scattered conversation data, requiring significant time and effort for manual review.
Innovation Solution
A multi-modal search-and-summarize tool that monitors conversational content of various formats, using multi-modal summarization models to generate summaries in response to user queries, thereby organizing and condensing conversation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually review voluminous conversation data, then they can access relevant information, but it requires significant time and effort
Solution Approach 1:
The system automatically generates summaries of conversation data without requiring user manual review. The summarization model processes conversation threads and produces condensed summaries that users can directly access, eliminating the need for manual reading and filtering of large volumes of conversation data.
Solution Approach 2:
The system extracts key information from voluminous conversation data by generating summaries that capture essential points. The summarization model identifies and extracts relevant content from large conversation threads, presenting only the critical information to users rather than requiring them to review all original messages.
2Loss of information
If users search through textual conversation data based on key terms, then they can access relevant conversation lines, but information presented is still scattered and disorganized
Solution Approach 1:
The system merges scattered conversation lines into organized summaries by processing multiple conversation threads and consolidating related information. The summarization model combines dispersed conversation elements into coherent, structured summaries that maintain relevance while improving organization and readability.
Solution Approach 2:
The system provides feedback by generating summaries that reflect the organized structure of conversation data. The summarization model processes scattered information and outputs reorganized content that maintains the logical flow and context of conversations, making information easier to navigate and understand.
3Loss of information
If existing chat systems provide hashtag functions for topic-based review, then users can access conversation data of certain topics, but information is still scattered and requires significant manual review effort
Solution Approach 1:
The system automatically processes and summarizes topic-related conversation data without requiring users to manually navigate through scattered messages. The summarization model independently organizes and presents consolidated information about specific topics, eliminating the need for users to manually review extensive conversation threads.
Solution Approach 2:
The system extracts and consolidates topic-related information from scattered conversation data. The summarization model identifies relevant conversation threads about specific topics and extracts essential information, presenting it in an organized summary format that improves productivity by reducing manual review requirements.
Data Source
AI summary
Embodiments described herein provide a multi-modal search-and-summarize tool for message platforms. Specifically, the multi-modal search-and-summarize tool may monitor conversational content of different formats, e.g., text, image, video, etc., and use multi-modal summarization models to generate a summary of the conversation channel. The summarization may be conducted via a search-and-summarize process in response to a specific user query, e.g., a user may enter “what did John and Josh say about the presentation tomorrow?” The multi-modal summarization model would first search for relevant conversation messages between user John and user Josh, identify communication files of different format (e.g., text messages, emojis, multimedia attachments, etc.), and then input the communication files to respective text or image encoders to generate a summary of the communication content.