Abstractive Summary Scheduling for Multi-Party Channel Navigation
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Solution Overview
Problem
Group chats and collaborative knowledge base environments generate overwhelming volumes of information, making it difficult for users to navigate and quickly grasp important context, especially in urgent situations like incident management.
Innovation Solution
A communication channel extraction and summary server system that uses natural language processing and text summarization machine learning models to generate abstractive context summaries for multi-party communication channels, providing low-latency summaries to new and returning users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users directly access multi-party communication channels with large volumes of information, then they can access complete communication data, but it becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent segments the large volume of communication data into two distinct parts: (1) an abstractive context summary that provides high-level overview and key information, and (2) the complete communication data objects. This segmentation allows users to first navigate the condensed summary easily, then access detailed information only when needed, resolving the contradiction between information completeness and navigation ease
Solution Approach 2:
The system extracts essential information from the complete communication data to generate an abstractive context summary. This extraction process separates the critical contextual information from the full data set, enabling users to quickly understand the communication channel's context without being overwhelmed by the complete data volume
2Loss of time
If the system generates abstractive context summaries using text summarization machine learning models, then users can quickly understand context, but computational expense increases
Solution Approach 1:
The system applies partial action by generating abstractive context summaries only for communication channels where users need contextual orientation, rather than processing all communication data universally. The summary generation is triggered selectively based on user actions (e.g., joining a channel, returning after absence), reducing unnecessary computational expense while still providing quick context understanding when needed
Solution Approach 2:
The system implements self-service by generating abstractive context summaries on-demand when users join or return to communication channels, rather than pre-processing all possible summaries. The machine learning model processes only the specific communication data objects relevant to the user's current needs, optimizing computational resource usage while maintaining fast context provision
3Productivity
If the system processes and generates summaries for all communication channels, then all users receive comprehensive summaries, but processing time and resources increase
Solution Approach 1:
The system applies local quality by customizing abstractive context summaries according to individual user profiles and their specific interaction patterns with communication channels. Each user receives summaries tailored to their role, preferences, and historical behavior, rather than uniform summaries for all users. This approach improves productivity by making summary generation more targeted and efficient
Data Source
AI summary
Methods, apparatuses, or computer program products provide for enabling generation of abstractive context summaries for multi-party communication channels. An abstractive context summary scheduling interface associated with a selected multi-party communication channel may be caused to be rendered to a client computing device associated with a member profile identifier. A summary generation parameter set may be received in response to user engagement with the abstractive context summary scheduling interface. A plurality of communication data objects from the selected multi-party communication channel may be extracted based on the summary generation parameter set. An abstractive context summary for the selected multi-party communication channel may be generated based on the plurality of communication data objects and utilizing a text summarization machine learning model. The abstractive context summary may be caused to be rendered for display on the client computing device associated with the member profile identifier.