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        <title>Structure-Infused Copy Mechanisms for Abstractive Summarization</title>
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        <namePart type="given">Kaiqiang</namePart>
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    <abstract>Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Structure-Infused Copy Mechanisms for Abstractive Summarization
%A Song, Kaiqiang
%A Zhao, Lin
%A Liu, Fei
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F song-etal-2018-structure
%X Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.
%U https://aclanthology.org/C18-1146
%P 1717-1729

Markdown (Informal)

[Structure-Infused Copy Mechanisms for Abstractive Summarization](https://aclanthology.org/C18-1146) (Song et al., COLING 2018)

• Structure-Infused Copy Mechanisms for Abstractive Summarization (Song et al., COLING 2018)

ACL

• Kaiqiang Song, Lin Zhao, and Fei Liu. 2018. Structure-Infused Copy Mechanisms for Abstractive Summarization. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1717–1729, Santa Fe, New Mexico, USA. Association for Computational Linguistics.