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        <title>Abstractive News Summarization based on Event Semantic Link Network</title>
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        <namePart type="given">Hai</namePart>
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        <dateIssued>2016-12</dateIssued>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.</abstract>
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        <url>https://aclanthology.org/C16-1023</url>
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%0 Conference Proceedings
%T Abstractive News Summarization based on Event Semantic Link Network
%A Li, Wei
%A He, Lei
%A Zhuge, Hai
%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 li-etal-2016-abstractive
%X This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.
%U https://aclanthology.org/C16-1023
%P 236-246

Markdown (Informal)

[Abstractive News Summarization based on Event Semantic Link Network](https://aclanthology.org/C16-1023) (Li et al., COLING 2016)

• Abstractive News Summarization based on Event Semantic Link Network (Li et al., COLING 2016)

ACL

• Wei Li, Lei He, and Hai Zhuge. 2016. Abstractive News Summarization based on Event Semantic Link Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 236–246, Osaka, Japan. The COLING 2016 Organizing Committee.