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SummPip: unsupervised multi-document summarization with sentence graph compression

conference contribution
posted on 2020-07-01, 00:00 authored by Jinming Zhao, Ming LiuMing Liu, Longxiang Gao, Yuan JinYuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari
Obtaining training data for multi-document Summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary. Experiments on Multi-News and DUC-2004 datasets show that our method is competitive to previous unsupervised methods and is even comparable to the neural supervised approaches. In addition, human evaluation shows our system produces consistent and complete summaries compared to human written ones.

History

Pagination

1949-1952

Location

Online, China

Start date

2020-07-25

End date

2020-07-30

ISBN-13

9781450380164

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

SIGIR 2020 : Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Event

Association for Computer Machinery Special Interest Group on Information Retrieval. Conference (43rd : 2020 : Online, China)

Publisher

Association for Computing Machinery

Place of publication

New York, N.Y.

Series

Association for Computer Machinery Special Interest Group on Information Retrieval Conference

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