Query-Focused Summarization via Extractor-Abstractor Segmentation
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Solution Overview
Problem
Existing query-focused summarization models are inefficient in generating summaries that accurately answer user-specified queries, often failing to capture all relevant content due to length limitations and reliance on two-step approaches that separate extraction and abstraction processes.
Innovation Solution
The implementation of a two-step query-focused summarization model that includes an extractor model to score and rank passages based on relevance, using a relevance model trained on QFS data, and an abstractor model to synthesize the final summary, along with an end-to-end encoder-decoder model that splits the source document into fixed-length segments for joint encoding and decoding, allowing for richer data representations and improved relevance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a two-step approach with separate extractor and abstractor models is used, then the summarization process can be more targeted to user queries, but the system complexity increases and processing efficiency decreases
Solution Approach 1:
The patent divides the summarization task into two distinct modules: an extractor model that identifies query-relevant passages and an abstractor model that generates the final summary. This segmentation allows each component to specialize in one aspect of the task, improving overall query relevance accuracy while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The extractor model serves as an intermediary component between the input document and the abstractor model. It processes the document to identify and extract relevant passages based on the query, then passes these extracted segments to the abstractor. This intermediary step improves precision by ensuring the abstractor only processes query-relevant information rather than the entire document.
2Loss of information
If the source document is processed in full by the model, then all relevant information can be captured, but the processing time and computational resources increase significantly
Solution Approach 1:
The extractor model extracts only the query-relevant passages from the source document, separating the essential information needed to answer the query from the rest of the document. This extraction process reduces the amount of text passed to the abstractor model, significantly decreasing processing time while maintaining coverage of all query-relevant information.
Solution Approach 2:
Instead of processing the entire document, the system applies partial action by processing only the extracted relevant passages. The extractor identifies and extracts specific segments that contain information necessary to answer the query, allowing the abstractor to focus computational resources on synthesizing these partial results rather than analyzing the complete document.
3Ease of operation
If the summary length is constrained to fit user needs, then the output becomes more usable, but relevant content may be omitted due to length limitations
Solution Approach 1:
The system applies local quality by concentrating summarization efforts on query-relevant passages rather than attempting to summarize the entire document uniformly. The extractor identifies specific local segments containing answers to the query, and the abstractor synthesizes these segments into a concise summary that fits user length constraints while preserving all locally relevant information.
Solution Approach 2:
The extractor model extracts only the specific passages that contain information necessary to answer the query, filtering out irrelevant content before summarization. This extraction ensures that the subsequent summary, even with length constraints, contains all essential relevant information while excluding unnecessary details, thereby maintaining both usability and completeness.
Data Source
AI summary
Embodiments described herein provide a query-focused summarization model that employs a single or dual encoder model. A two-step approach may be adopted that first extracts parts of the source document and then synthesizes the extracted segments into a final summary. In another embodiment, an end-to-end approach may be adopted that splits the source document into overlapping segments, and then concatenates encodings into a single embedding sequence for the decoder to output a summary.