A Proposition-Level Clustering Approach for Multi-Document Summarization

• URL: http://arxiv.org/abs/2112.08770v1
• Date: Thu, 16 Dec 2021 10:34:22 GMT
• Title: A Proposition-Level Clustering Approach for Multi-Document Summarization
• Authors: Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger and Ido Dagan
• Abstract summary: We revisit the clustering approach, grouping together propositions for more precise information alignment. Our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster by fusing its propositions. Our summarization method improves over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011 datasets.
• Score: 82.4616498914049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Clusters were leveraged to indicate information saliency and to avoid redundancy. These methods focused on clustering sentences, even though closely related sentences also usually contain non-aligning information. In this work, we revisit the clustering approach, grouping together propositions for more precise information alignment. Specifically, our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster by fusing its propositions. Our summarization method improves over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011 datasets, both in automatic ROUGE scores and human preference.

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