Collaborative Moment Summarization via Burst Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users in persistent collaboration spaces face challenges in managing and reviewing conversation history due to distractions, leading to a need for efficient summarization of conversation threads to quickly understand the context upon returning to a conversation.
Innovation Solution
A method that groups messages into 'moments' using keyword and entity extractors, natural language processing, and machine learning to identify start and end points of bursts, allowing for the generation of summaries that can be transmitted to users, thereby reducing the burden of reviewing extensive conversation transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users review entire conversation transcripts to understand context, then completeness of information understanding is improved, but time consumption and user burden increase significantly
Solution Approach 1:
The system extracts only the most relevant information from conversation transcripts by identifying key moments and generating concise summaries. Natural language processing techniques extract essential content while filtering out redundant details, allowing users to obtain complete contextual understanding without reading entire transcripts.
Solution Approach 2:
The system transforms conversation data from raw message format to structured summary format with changed parameters including condensed length, organized temporal structure, and highlighted key information. This parameter transformation maintains information completeness while dramatically reducing the volume users must process.
2Productivity
If conversation threads are summarized using natural language processing, then user understanding speed is improved, but system processing complexity increases
Solution Approach 1:
The system segments conversation threads into discrete moments based on temporal boundaries and topic transitions. By dividing continuous conversations into manageable segments, the NLP processing complexity is reduced while maintaining the ability to generate comprehensive summaries that capture essential information from each segment.
Solution Approach 2:
The system introduces an intermediary processing layer that bridges raw conversation data and user-comprehensible summaries. This intermediary layer uses automated moment identification and summary generation techniques to translate complex multi-user interactions into simplified narratives, reducing the computational burden on users while preserving meaning.
3Ease of operation
If messages are grouped into moments with keywords and entities, then searchability and notification management are improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary processing of conversation data by pre-identifying moments, extracting keywords, and tagging entities as messages are received. This advance organization creates an indexed structure that enables rapid searching and filtering without requiring intensive processing when users initiate search operations, thus improving ease of operation while managing data processing volume efficiently.
Data Source
AI summary
A method, computer system, and computer program product for cognitively summarizing one or more collaborative moments is provided. An embodiment may include receiving a plurality of communications sent during a collaborative session. The embodiment may also include grouping the plurality of received communications into one or more bursts. The embodiment may further include assigning one or more keywords or one or more entities to each burst. The embodiment may also include generating a summary for each burst using one or more natural language processing techniques based on the assigned one or more keywords or one or more entities. The embodiment may further include transmitting the generated summary to each user participating in the collaborative session.