Dialogue Summarization Using Word Graphs and Speaker Role Inference
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Solution Overview
Problem
Existing dialogue summarization systems fail to directly utilize speaker information and require external systems for summarizing long texts, relying on algorithms like WordNet and TF-IDF for keyword importance, and additional tasks such as submodularity maximization or entailment labeling.
Innovation Solution
A method that generates a word graph from dialogue information, extracts keywords, and calculates scores for candidate summary sentences based on keyword relevance, using k-shortest path algorithms and graph degeneracy to select summary sentences, while converting grammatical person to a consistent third person for improved summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing supervised learning summarization architecture is used, then summarization can be performed with learned data, but it cannot be used in domains without learning data
Solution Approach 1:
The system performs unsupervised learning to automatically identify speaker roles and generate summaries without requiring pre-labeled training data. The word graph construction and speaker role inference algorithms enable the system to serve itself by deriving structure and meaning directly from raw dialogue text.
Solution Approach 2:
The approach changes the fundamental parameters of summarization by shifting from supervised learning with labeled data to unsupervised learning with automatic speaker role inference. This parameter change enables domain adaptability while maintaining summarization quality through alternative computational mechanisms.
2Measurement precision
If external systems like WordNet and TF-IDF are used for keyword extraction, then keyword importance can be determined, but additional external dependencies and complexity are introduced
Solution Approach 1:
The patent extracts and removes the dependency on external systems by implementing keyword extraction and importance determination directly within the word graph framework. The speaker role inference algorithm identifies key terms and phrases without requiring WordNet, TF-IDF, or other external resources.
Solution Approach 2:
The word graph structure serves multiple functions simultaneously: it represents dialogue content, identifies speaker roles, extracts keywords, and generates summaries. This multi-functionality eliminates the need for separate external systems for each task.
3Manufacturing precision
If additional tasks like submodularity maximization or entailment labeling are required for sentence extraction, then summary quality can be improved, but processing time and complexity increase
Solution Approach 1:
The patent merges multiple summary generation tasks into a unified word graph-based framework. Speaker role inference, keyword extraction, and summary sentence selection are combined into a single integrated process that operates directly on the word graph structure without requiring separate submodularity maximization or entailment labeling steps.
Solution Approach 2:
The approach skips complex intermediate processing steps by directly generating summaries from the word graph using speaker role information. This bypasses time-consuming operations like submodularity maximization and entailment labeling while maintaining summary quality through the structured word graph representation.
4Loss of information
If speaker information is melted into speaking indirectly, then dialogue context is preserved, but direct utilization of speaker information for summary is lost
Solution Approach 1:
The system performs preliminary speaker role inference by analyzing the word graph structure before summary generation. This preliminary action identifies and labels speaker roles (narrator, interviewer, interviewee, etc.) explicitly, making speaker information directly available for the summarization process rather than leaving it embedded in the text.
Solution Approach 2:
The word graph serves as an intermediary structure that explicitly represents speaker information. By constructing the word graph with speaker-specific nodes and edges, the system creates a mediating representation that makes implicit speaker information explicit and directly usable for summary generation.
Data Source
AI summary
Disclosed is a method for dialogued summarization with word graphs, which is performed by one or more processors of a computing device. The method may include: generating a word graph based on information on a dialogue which is a summary target; extracting at least one keyword based on the generated word graph; generating a plurality of candidate summary sentences based on the generated word graph; and calculating a score associated with at least one keyword for each of the plurality of candidate summary sentences, and selecting at least one of the plurality of candidate summary sentences based on the calculated score.