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        <title>A Redundancy-Aware Sentence Regression Framework for Extractive Summarization</title>
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    <name type="personal">
        <namePart type="given">Pengjie</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences. In this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f(s|S) of a sentence s given a set of selected sentences S. Specifically, we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric. Besides the single sentence features, additional features derived from the sentence relations are incorporated. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that the proposed method outperforms state-of-the-art extractive summarization approaches.</abstract>
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        <url>https://aclanthology.org/C16-1004</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T A Redundancy-Aware Sentence Regression Framework for Extractive Summarization
%A Ren, Pengjie
%A Wei, Furu
%A Chen, Zhumin
%A Ma, Jun
%A Zhou, Ming
%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 ren-etal-2016-redundancy
%X Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences. In this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f(s|S) of a sentence s given a set of selected sentences S. Specifically, we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric. Besides the single sentence features, additional features derived from the sentence relations are incorporated. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that the proposed method outperforms state-of-the-art extractive summarization approaches.
%U https://aclanthology.org/C16-1004
%P 33-43

Markdown (Informal)

[A Redundancy-Aware Sentence Regression Framework for Extractive Summarization](https://aclanthology.org/C16-1004) (Ren et al., COLING 2016)

• A Redundancy-Aware Sentence Regression Framework for Extractive Summarization (Ren et al., COLING 2016)

ACL

• Pengjie Ren, Furu Wei, Zhumin Chen, Jun Ma, and Ming Zhou. 2016. A Redundancy-Aware Sentence Regression Framework for Extractive Summarization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 33–43, Osaka, Japan. The COLING 2016 Organizing Committee.