Multi-document Summary Generation Using Cascaded Attention
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Solution Overview
Problem
Existing multi-document summarization methods generate summaries with a high amount of redundancy due to inaccurate eigenword extraction, affecting the accuracy of similarity calculations between candidate sentences.
Innovation Solution
A cascaded attention mechanism and unsupervised learning model are used to train candidate sentences, optimizing a reconstruction error function to determine the importance of each sentence and reduce redundancy by filtering out less important words and phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If word vector representation of eigenword is used to calculate similarity between candidate sentences, then the summarization process can be automated, but accuracy of similarity calculation deteriorates when eigenword extraction is inaccurate
Solution Approach 1:
The patent replaces the traditional word vector representation method with a cascaded attention mechanism that directly processes candidate sentences. Instead of relying on eigenword extraction and word vectors, the system uses neural network layers with attention mechanisms to compute sentence similarities, thereby eliminating the inaccuracies introduced by eigenword extraction while maintaining automated operation.
2Productivity
If candidate sentences are processed using traditional methods, then the summarization can be generated, but redundancy information in the summary increases
Solution Approach 1:
The patent applies local quality by using the cascaded attention mechanism to assign different weights to different parts of the candidate sentences based on their importance. The attention mechanism dynamically adjusts the focus on specific words and phrases within each sentence, allowing the system to retain only the most relevant information while discarding redundancy, thereby improving summary quality without sacrificing generation efficiency.
3Ease of manufacture
If eigenword extraction is performed, then the summarization process can proceed, but accuracy of importance estimation deteriorates
Solution Approach 1:
The patent extracts and eliminates the problematic eigenword extraction step from the summarization process. By removing this inaccurate component and replacing it with direct sentence processing through the cascaded attention mechanism, the system maintains ease of operation while significantly improving importance estimation accuracy through direct analysis of candidate sentence structures and meanings.
Data Source
AI summary
A multi-document summary generation method includes obtaining a candidate sentence set, training each candidate sentence in the candidate sentence set using a cascaded attention mechanism and an unsupervised learning model in a preset network model, to obtain importance of each candidate sentence, selecting, based on the importance of each candidate sentence, a phrase that meets a preset condition from the candidate sentence set as a summary phrase set, and obtaining a summary of a plurality of candidate documents based on the summary phrase set.