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        <title>Neural Related Work Summarization with a Joint Context-driven Attention Mechanism</title>
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        <namePart type="given">Yongzhen</namePart>
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        <namePart type="given">Xiaozhong</namePart>
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    <name type="personal">
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1204</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Neural Related Work Summarization with a Joint Context-driven Attention Mechanism
%A Wang, Yongzhen
%A Liu, Xiaozhong
%A Gao, Zheng
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-etal-2018-neural-related
%X Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.
%R 10.18653/v1/D18-1204
%U https://aclanthology.org/D18-1204
%U https://doi.org/10.18653/v1/D18-1204
%P 1776-1786

Markdown (Informal)

[Neural Related Work Summarization with a Joint Context-driven Attention Mechanism](https://aclanthology.org/D18-1204) (Wang et al., EMNLP 2018)

• Neural Related Work Summarization with a Joint Context-driven Attention Mechanism (Wang et al., EMNLP 2018)

ACL

• Yongzhen Wang, Xiaozhong Liu, and Zheng Gao. 2018. Neural Related Work Summarization with a Joint Context-driven Attention Mechanism. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1776–1786, Brussels, Belgium. Association for Computational Linguistics.