SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

• URL: http://arxiv.org/abs/2007.08954v2
• Date: Mon, 20 Jul 2020 10:20:12 GMT
• Title: SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
• Authors: Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang and Gholamreza Haffari
• Abstract summary: SummPip is an unsupervised method for multi-document summarization. We convert the original documents to a sentence graph, taking both linguistic and deep representation into account. We then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary.
• Score: 61.97200991151141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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