Multi-Document Summarization Clustering Contradiction Masking
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Solution Overview
Problem
Current neural summarization techniques for multi-document summarization are prone to factual errors and lack explainability, making them difficult to use in practice.
Innovation Solution
A multi-document summarization approach that clusters sentences from various documents using a clustering model, aligns them with a reference summary, masks contradictory sentences, and uses a pretrained model to generate a summary that is consistent with all source documents, providing an interpretable explanation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current neural summarization techniques are used, then summarization can be performed, but factual errors and hallucinations occur
Solution Approach 1:
The system uses feedback mechanisms where generated summaries are compared against source documents and reference summaries. The loss function incorporates feedback about factual accuracy, allowing the model to learn from discrepancies and reduce hallucinations in subsequent generations.
Solution Approach 2:
The system performs preliminary actions by clustering sentences from source documents before generation, and by generating reference summaries for each cluster in advance. This preliminary organization ensures that the final summary is grounded in actual document content, reducing factual errors.
2Ease of operation
If current neural summarization techniques are used, then summaries can be generated, but no explanation is provided for how the summary was developed
Solution Approach 1:
The system segments the summarization process into distinct stages: sentence clustering, reference summary generation for each cluster, and final summary generation. This segmentation allows the system to track and explain which source sentences contribute to which parts of the final summary, providing transparency about the summarization process.
Solution Approach 2:
The system introduces an intermediary layer of reference summaries for each cluster as a mediator between source documents and the final summary. This intermediary structure serves as an explanation mechanism, showing users how the final summary is derived from source material through intermediate steps.
3Reliability
If sentences are masked based on contradictions, then factual accuracy improves, but the complexity of the summarization process increases
Solution Approach 1:
The system performs preliminary contradiction detection and sentence masking before the main summarization process. By identifying and masking contradictory sentences in advance during the clustering phase, the system reduces the complexity of the main generation process while maintaining high factual accuracy.
Data Source
AI summary
Embodiments described herein provide methods and systems for summarizing multiple documents. A system receives a plurality of documents and generates embeddings of the sentences from the plurality of documents. The embedded sentences are clustered in a representation space. Sentences from a reference summary are embedded and aligned with the closest cluster. Sentences from each cluster are summarized with the aligned reference sentences as a target. A loss is computed based on the summarized sentences and the aligned references, and the natural language processing model is updated based on the loss. Sentences may be masked from being used in the summarization by identifying sentences that are contradicted by other sentences within the plurality of documents.