Keyword-Based Dialogue Summarizer for Telecommunications
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Solution Overview
Problem
Manual summarization of telecommunications conversations by business agents is time-consuming and often lacks relevance, failing to accurately capture transactional significance and roles of participants.
Innovation Solution
A keyword-based dialogue summarization model using machine learning to generate summaries from transcripts, where predefined keywords are encoded separately from the text, allowing for role-based summarization and highlighting of relevant conversation segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual summarization is performed by agents, then summaries can be generated, but it is time-consuming and reduces productivity
Solution Approach 1:
The patent replaces the manual mechanical summarization process with an automated machine learning system. The model takes conversation transcripts and keywords as input, processes them through trained encoders, and automatically generates accurate summaries without human intervention, thereby eliminating time consumption while maintaining reliability
Solution Approach 2:
The system enables self-service summarization where the machine learning model autonomously processes transcripts and generates summaries without requiring agent involvement. The automated system serves itself by taking raw conversation data and producing structured summaries with role identification and transactional significance detection
2Productivity
If generic summarization models are used, then automation is achieved, but the summaries lack relevance and fail to capture transactional significance
Solution Approach 1:
The patent segments the summarization process by introducing keyword-based segmentation that divides the conversation into relevant and irrelevant portions. The model identifies segments containing transactional significance related to specific keywords (e.g., pricing, availability, booking) and focuses summarization on these segments, ensuring relevance while maintaining automation
Solution Approach 2:
The system applies local quality by enhancing specific portions of the summary that contain transactionally significant information. Rather than treating all content uniformly, the model identifies and emphasizes segments related to predefined keywords, giving them higher quality and relevance in the final summary output
3Reliability
If manual summarization is performed, then role identification can be achieved, but it increases device complexity and operation difficulty
Solution Approach 1:
The patent replaces complex manual role identification processes with automated machine learning-based role detection. The trained model automatically analyzes conversation patterns, speaker relationships, and contextual cues to identify roles (e.g., customer, agent, booking entity) without requiring complex manual analysis systems
4Reliability
If agents manually review and select summary content, then accuracy can be maintained, but productivity decreases due to additional time required
Solution Approach 1:
The system provides self-service summarization where the machine learning model autonomously generates accurate summaries without requiring agent review or selection. The automated system handles the entire process from transcript input to finalized summary output, maintaining accuracy through trained algorithms while eliminating the time agents would spend reviewing and selecting content
Data Source
AI summary
A method of generating keyword-based dialogue summaries is provided. The method includes inputting a transcript of an audio conversation and a keyword into a machine learning model trained based on encodings representing the keyword and the transcript, generating computer-generated text different from and semantically descriptive of the transcript and semantically associated with the keyword, and outputting the computer-generated text in association with a selectable item selectable for inclusion of the computer-generated text in displayed text representing the transcript, the selectable item associated with the keyword.