Diarized Conversation Summarization via Segmentation and Clustering
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Solution Overview
Problem
Conventional systems for summarizing recorded calls and transcripts are inefficient due to high computational resource usage and inability to effectively handle large text blocks, failing to differentiate speakers and remove redundancies, which hampers accuracy and speed in review processes, especially in sales and other professional contexts.
Innovation Solution
A method involving diarization of conversations, classification of textual monologues using natural language processing, clustering for redundancy removal, and scoring for importance, presenting summaries categorized and ranked to enhance processing speed and accuracy, utilizing machine learning and heuristic techniques to adapt to specific industry needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional text rephrasing algorithms are used, then processing is simplified, but computational resource usage becomes unmanageable and memory consumption increases significantly for large text blocks
Solution Approach 1:
The patent segments the transcript into diarized monologues by speaker, then further segments each monologue into sentences. This segmentation allows the system to process text in manageable chunks rather than handling large blocks at once, reducing memory consumption and computational resource usage while maintaining processing effectiveness.
Solution Approach 2:
The patent extracts and removes redundant sentences from the transcript through clustering and redundancy detection. By identifying and removing redundant content, the system reduces the amount of text that requires processing, thereby decreasing computational resource usage and memory consumption while preserving essential information.
2Measurement precision
If complete sales calls are listened to manually, then review accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
The patent extracts key information from the transcript by identifying and selecting important sentences through classification and redundancy removal. This extraction creates a condensed summary that maintains review accuracy while significantly reducing the time required compared to listening to complete calls manually.
Solution Approach 2:
The patent introduces an intermediary summary representation that bridges the gap between complete transcripts and manual review. The summarized output serves as an intermediary product that preserves essential information and review accuracy while reducing time consumption, allowing managers to review calls efficiently without sacrificing important details.
3Measurement precision
If diarization and classification are performed, then speaker differentiation and redundancy removal improve accuracy, but processing complexity increases
Solution Approach 1:
The patent segments the processing into distinct sequential steps: diarization (grouping by speaker), classification (categorizing monologues), sentence extraction, clustering (grouping similar sentences), and redundancy removal. This segmentation of complex processing into manageable steps maintains high summary accuracy while making the overall process more manageable and less complex to implement.
Solution Approach 2:
The patent performs preliminary diarization and classification actions before final summary generation. By completing these preparatory steps in advance, the system organizes the data structure beforehand, which simplifies subsequent processing and reduces overall processing complexity while maintaining high accuracy in the final summary.
4Ease of operation
If conventional attention mechanisms are used, then processing is straightforward, but handling of longer dialogues becomes computationally intensive
Solution Approach 1:
The patent segments the dialogue into speaker-specific monologues and then into individual sentences. This segmentation allows the system to process longer dialogues by handling smaller, manageable units rather than processing the entire dialogue at once, reducing computational intensity while maintaining processing straightforwardness.
Solution Approach 2:
The patent extracts and processes only the essential information from each monologue through classification and redundancy removal, rather than processing every detail of the entire dialogue. This extraction approach reduces computational intensity for longer dialogues while maintaining processing efficiency through focused analysis of key content.
Data Source
AI summary
Methods for generating a categorized, ranked, condensed summary of a transcript of a conversation, involving obtaining a diarized version of the transcript of the conversation, storing textual monologues from the transcript, determining classifications as to the textual monologues based on a classifier algorithm, associating the classifications with the textual monologues, creating textually-modified rephrasings of the textual monologues based on text and classification thereof, storing the textually-modified rephrasings, aggregating the textually-modified rephrasings based on associated clustering and scoring, and transmitting summary information pertaining to the aggregated textually-modified rephrasings to a user device.