Unsupervised Multi-Document Summarization via Cross-Entropy
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Solution Overview
Problem
Current natural language processing methods for summarization require domain knowledge and rely on supervised training data, limiting the effectiveness of unsupervised extractive summarization in generating high-quality summaries without modification of text fragments.
Innovation Solution
A method utilizing the cross-entropy method for generating multi-document summaries by identifying keywords, selecting relevant sentences, and calculating quality scores to produce a summary that satisfies user-defined goals and constraints, employing an unsupervised, extractive-based approach without the need for domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training data and domain knowledge are used for summarization, then summary quality is improved, but system complexity and data requirements increase
Solution Approach 1:
The system performs self-service by automatically selecting and scoring sentences based on cross-entropy calculations and quality features without requiring external domain knowledge or supervised training data. The unsupervised extractive approach enables the system to independently generate summaries by evaluating sentence relevance to the query and combining multiple quality metrics.
2Measurement precision
If supervised training data is used for summarization, then summary quality is improved, but availability of training data decreases
Solution Approach 1:
The system operates autonomously without external training data by using unsupervised learnable parameters that are automatically adjusted during the summarization process. The cross-entropy method and quality feature evaluation enable the system to adapt to different domains and queries without requiring pre-trained models or domain-specific training corpora.
3Ease of operation
If extractive-based unsupervised summarization is used, then ease of operation is improved, but summary quality deteriorates
Solution Approach 1:
The system segments the summarization process into distinct components: sentence selection based on cross-entropy calculation, quality feature evaluation (relevance, diversity, coherence), and candidate summary generation. This segmentation allows each component to be optimized independently while maintaining overall system simplicity and extractive-based operation.
Solution Approach 2:
The system improves summary quality by dynamically adjusting multiple parameters including cross-entropy thresholds, quality feature weights, and sentence selection criteria. These parameter changes enable the system to adapt to different queries and documents while maintaining ease of operation through automated parameter optimization.
Data Source
AI summary
A method, computer system, and computer program product for generating a multi-document summary is provided. The embodiment may include receiving a query statement, one or more documents, one or more summary constraints, and quality goals. The embodiment may include identifying one or more keywords within the query statement. The embodiment may include performing a sentence selection from the one or more documents based on the one or more identified keywords. The embodiment may include generating a plurality of candidate summaries of the one or more documents based on the performed sentence selection, the goals, and a cross entropy method. The embodiment may include calculating a quality score for each of the plurality of generated candidate summaries using a plurality of quality features. The embodiment may include selecting a candidate summary from the plurality of generated candidate summaries with the highest calculated quality score that also satisfies a quality score threshold.