Controllable Multimodal Meeting Summarization with Semantic Entities
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Solution Overview
Problem
Current note-taking systems lack real-time incremental capabilities for summarizing meeting minutes, relying on post-meeting manual tasks and lacking controllability and multimodality in inputs, with a dependency on existing training data for new language domains.
Innovation Solution
A controllable multimodal meeting summarization system that uses machine learning to generate summaries by adjusting a pre-existing language model through domain adaptation and noise injection, incorporating multimodal inputs and user-controlled variables for real-time note-taking and summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-meeting manual summarization is used, then accuracy of meeting minutes can be maintained, but productivity and real-time capabilities deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously analyzing meeting audio and generating summaries in real-time during the meeting, rather than waiting for post-meeting processing. The incremental summarization updates the meeting summary continuously as the meeting progresses, ensuring both real-time productivity and accurate capture of meeting content.
Solution Approach 2:
The patent replaces the mechanical manual summarization process with an automated machine learning system that uses audio analysis, transcription, and natural language generation to create meeting summaries automatically. This substitution eliminates manual labor while maintaining or improving accuracy through automated content analysis.
2Measurement precision
If domain-specific language models are used, then accuracy for specific domains improves, but device complexity and training data requirements worsen
Solution Approach 1:
The system applies local quality by incorporating domain-specific vocabulary and context only where needed in the summarization process, rather than requiring complete domain-specific models. The approach enhances the base model with targeted domain knowledge through vocabulary adjustments and context-aware processing, reducing overall complexity while improving domain accuracy.
Solution Approach 2:
The patent employs a universal base language model that can adapt to multiple domains through prompt engineering and context injection rather than requiring separate domain-specific models. This multi-functional approach allows the same model architecture to serve different domains, reducing device complexity and training data requirements while maintaining domain-specific accuracy.
3Measurement precision
If comprehensive audio analysis is performed, then summarization accuracy improves, but processing time and energy consumption worsen
Solution Approach 1:
The system extracts only the essential audio signals and transcription content needed for summarization, rather than processing all audio data comprehensively. By focusing on key speech segments and using efficient natural language processing on extracted text, the system maintains high accuracy while reducing energy consumption associated with full audio analysis.
Solution Approach 2:
The patent applies partial action by performing audio analysis and summarization at incremental intervals rather than continuously analyzing every audio segment in real-time. The incremental summarization updates the meeting summary at appropriate intervals, balancing accuracy with reduced processing energy requirements compared to continuous comprehensive analysis.
Data Source
AI summary
Disclosed is a technical solution to summarize a multimodal conferencing environment. The solution is designed to improve efficiency and accuracy of computing systems as a summarization tool by incorporating memory, machine readable instructions, and processor circuitry. The solution executes the functions of adjusting a language model based on a terminology utilized in a first context data; generating a conversation summary from a transcription and a human controlled variable; extracting a semantic entity from the conversation summary and second context data, where the second context data is indicative of an input associated with a conferencing environment; and summarize the semantic entity and the second context data using the adjusted language model.