@inproceedings{wang-etal-2016-exploring,
title = "Exploring Text Links for Coherent Multi-Document Summarization",
author = "Wang, Xun and
Nishino, Masaaki and
Hirao, Tsutomu and
Sudoh, Katsuhito and
Nagata, Masaaki",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1021",
pages = "213--223",
abstract = "Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.",
}
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<abstract>Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.</abstract>
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%0 Conference Proceedings
%T Exploring Text Links for Coherent Multi-Document Summarization
%A Wang, Xun
%A Nishino, Masaaki
%A Hirao, Tsutomu
%A Sudoh, Katsuhito
%A Nagata, Masaaki
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wang-etal-2016-exploring
%X Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.
%U https://aclanthology.org/C16-1021
%P 213-223
Markdown (Informal)
[Exploring Text Links for Coherent Multi-Document Summarization](https://aclanthology.org/C16-1021) (Wang et al., COLING 2016)
ACL
- Xun Wang, Masaaki Nishino, Tsutomu Hirao, Katsuhito Sudoh, and Masaaki Nagata. 2016. Exploring Text Links for Coherent Multi-Document Summarization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 213–223, Osaka, Japan. The COLING 2016 Organizing Committee.