Generative Text Summarization with Best-First Search and SBWR
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Solution Overview
Problem
Current text summarization systems face challenges in generating accurate and informative summaries that balance length and content relevance, often resulting in overly short or abstract summaries that lose key information.
Innovation Solution
A generative text summarization model employing a best-first search algorithm and a soft-bounded word-reward (SBWR) algorithm to enlarge the search space and re-rank candidate words, incorporating a brevity penalty and transformer neural model for improved output, ensuring summaries meet length thresholds and maintain contextual accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If traditional text summarization algorithms are used to generate concise summaries, then the summary length is reduced, but the accuracy and completeness of key information deteriorates
Solution Approach 1:
The patent applies dynamic search space enlargement where the candidate word pool is adaptively expanded beyond the immediate context window. The system dynamically adjusts the search radius to include words from broader contextual ranges, allowing the summarization process to access more potential candidate words while maintaining relevance. This dynamic approach enables the system to find optimal word selections that preserve key information while achieving concise summary lengths.
Solution Approach 2:
The patent introduces an additional dimension to the search process by considering words from multiple contextual distances simultaneously. Instead of selecting words only from immediate proximity to reference points, the system searches across multiple dimensional ranges in the text structure. This multi-dimensional search approach allows the system to discover important words that might be slightly farther from traditional reference points but still crucial for accurate summarization.
2Speed
If the search space for candidate words is limited to immediate context, then the processing speed is improved, but the quality of summary generation deteriorates
Solution Approach 1:
The patent segments the search space into multiple hierarchical levels: immediate context words, nearby context words, and broader context words. The system processes these segments in a structured sequence, starting with the most relevant immediate context and progressively incorporating words from wider ranges. This segmentation allows the system to maintain processing efficiency by handling close words first while still incorporating broader contextual information for improved summary quality.
Solution Approach 2:
The patent performs preliminary identification of reference points and candidate words from immediate context before expanding the search to broader contexts. By pre-processing and identifying high-probability candidate words from the most relevant contextual regions first, the system establishes a foundation for summary generation that can then be refined with additional words from extended search spaces, maintaining speed while improving quality.
3Device complexity
If fixed reward values are used for word selection, then the algorithm complexity is reduced, but the adaptability to different summary length requirements deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment where reward values for candidate words are modified based on the current summary length relative to the target threshold. As the summary approaches or exceeds the desired length, the system automatically adjusts reward parameters to favor more concise word selections. This parameter change mechanism allows the same algorithm to adapt to different length requirements without requiring complete reconfiguration, balancing complexity with versatility.
Data Source
AI summary
A generative automatic text summarization system and method is disclosed that may adopt a search and reranking strategy to improve the performance of a summarization task. The system and method may employ a transformer neural model to assist with the summarization task. The transformer neural model may be trained to learn human abstracts and may then be operable to generate abstractive summaries. With multiple summary hypothesis generated, a best-first search algorithm and reranking algorithm may be employed to select the best candidate summary as part of the output summary.