Multi-Speaker Conversation Summarization via Speaker Segmentation
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Solution Overview
Problem
Customer service conversation transcripts are often lengthy, improperly punctuated, and contain irrelevant or grammatically incorrect information, leading to information overload and confusion.
Innovation Solution
A summarization system that uses a modified Bidirectional Encoder Representations from Transformers (BERT) model to identify speaker sections, punctuate, and generate summaries by extracting key terms from transcripts of multi-speaker conversations, conserving resources and improving clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conversation transcripts are analyzed in full detail, then comprehensive customer service analysis is achieved, but information overload and processing complexity increase
Solution Approach 1:
The patent segments the conversation transcript into individual speaker sections using speaker identification. Each speaker's portion is processed separately through summarization, reducing the complexity of analyzing the entire transcript at once while preserving all information. This divides the large-scale problem into manageable smaller units.
Solution Approach 2:
The patent extracts key terms and generates summaries from each speaker section, separating the essential information from the full transcript. This extraction process removes redundant and irrelevant information while retaining the core meaningful content for analysis.
2Loss of information
If full transcripts are processed and stored, then complete conversation data is available, but computing and networking resources are consumed
Solution Approach 1:
The patent extracts only the essential key terms and generates condensed summaries from the full transcripts. This extraction eliminates the need to store and process complete transcripts, significantly reducing computing and networking resources while maintaining data availability in summarized form.
Solution Approach 2:
The patent applies different processing qualities to different parts of the transcript - full detail is retained for speaker identification metadata, while the conversational content is summarized. This local differentiation optimizes resource usage by applying heavy processing only where necessary.
3Ease of operation
If speaker sections are separated and processed individually, then clarity and resource efficiency improve, but system complexity increases
Solution Approach 1:
The patent segments transcripts by speaker and processes each section through the summarization system independently. This segmentation improves clarity by organizing output by speaker while the modular architecture manages complexity by reusing the same summarization components for each segment.
Solution Approach 2:
The patent uses a universal summarization model that handles multiple speaker sections with the same processing pipeline. This multi-functional approach improves clarity through consistent processing while managing complexity by avoiding the need for separate specialized systems for each speaker.
Data Source
AI summary
A system may separate a transcript of a conversation into a first section corresponding to a first speaker in the conversation, and a second section corresponding to a second speaker in the conversation. The system may process, based on delimiters and punctuation marks, the first section and the second section to form a first speaker transcript and a second speaker transcript; determine, based on one or more topic models, a first set of key terms associated with the first speaker transcript and a second set of key terms associated with the second speaker transcript. The system may generate, based on a common set of key terms associated with the first set and the second of key terms, a first transcript summary of the first speaker transcript and a second transcript summary of the second speaker transcript; and generate a summary based on the first and the second transcript summaries.