Hybrid Dialogue Summary Generation via Pre-defined Queries
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Solution Overview
Problem
Traditional summary generation methods for dialogues either prioritize recall and completeness using an extractive approach or coherence and readability using an abstractive approach, but often fail to achieve both simultaneously, leading to suboptimal summaries.
Innovation Solution
A method that combines extractive and abstractive approaches for summary generation, guided by pre-defined queries, where question-answer pairs are identified and used to generate summaries that focus on key questions and answers, leveraging offline and runtime processes to ensure relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If an extractive approach is used for summary generation, then recall and completeness are improved, but coherence and readability deteriorate
Solution Approach 1:
The patent combines extractive and abstractive approaches into a hybrid system. The extractive component identifies and preserves key information from the dialogue, while the abstractive component generates coherent natural language summaries. This merging allows the system to achieve both high recall through information extraction and good coherence through natural language generation, resolving the contradiction between these two opposing requirements.
Solution Approach 2:
The summary generation process is segmented into distinct stages: information extraction phase where key phrases and facts are identified from the dialogue, and summary generation phase where these extracted elements are organized into coherent natural language. This segmentation allows each component to optimize for its specific function - extraction for completeness and generation for readability - while contributing to the overall quality of the summary.
2Ease of operation
If an abstractive approach is used for summary generation, then coherence and readability are improved, but recall and completeness deteriorate
Solution Approach 1:
Before generating the abstractive summary, the system performs preliminary extraction of key information, phrases, and facts from the dialogue. This preliminary action ensures that important content is captured and stored, so that when the abstractive generation occurs, it can draw upon these extracted elements to maintain both coherence and recall, preventing information loss during the transformation to natural language.
Solution Approach 2:
The extracted key information serves as an intermediary between the original dialogue and the final abstractive summary. This intermediary layer preserves critical content from the source material while allowing the generation component to create coherent natural language output, thus mediating between the requirements for recall and readability.
3Reliability
If traditional extractive or abstractive methods are used, then one aspect of summary quality is improved, but the ability to achieve both recall and coherence simultaneously deteriorates
Solution Approach 1:
The patent creates a composite summary generation system that integrates multiple approaches (extractive and abstractive) into a unified hybrid model. This composite system leverages the strengths of each individual approach - the information preservation capability of extraction and the natural language fluency of abstraction - to produce summaries that simultaneously achieve high recall and coherence, something neither traditional method could accomplish alone.
Solution Approach 2:
The system dynamically adjusts the balance between extractive and abstractive components based on the specific dialogue characteristics and summarization requirements. By changing the parameters controlling the contribution of each approach, the system can optimize for different aspects of summary quality as needed, achieving adaptability across various dialogue types and summary objectives.
Data Source
AI summary
A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.