Proposition-Level Clustering for Multi-Document Summarization

Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan


Abstract
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, clusters were leveraged to indicate information saliency as well as to avoid redundancy. Such prior methods focused on clustering sentences, even though closely related sentences usually contain also non-aligned parts. In this work, we revisit the clustering approach, grouping together sub-sentential propositions, aiming at more precise information alignment. Specifically, our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster via text fusion.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.
Anthology ID:
2022.naacl-main.128
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1765–1779
Language:
URL:
https://aclanthology.org/2022.naacl-main.128
DOI:
10.18653/v1/2022.naacl-main.128
Bibkey:
Cite (ACL):
Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, and Ido Dagan. 2022. Proposition-Level Clustering for Multi-Document Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1765–1779, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Proposition-Level Clustering for Multi-Document Summarization (Ernst et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.128.pdf
Code
 oriern/procluster +  additional community code