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        <title>Controlling Length in Abstractive Summarization Using a Convolutional Neural Network</title>
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        <namePart type="given">Yizhu</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length constraints, the ability to control the summary length in abstractive summarization is an important problem. In this paper, we propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. The results show that this approach generates high-quality summaries with user defined length, and outperforms the baselines consistently in terms of ROUGE score, length variations and semantic similarity.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1444</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
%A Liu, Yizhu
%A Luo, Zhiyi
%A Zhu, Kenny
%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 liu-etal-2018-controlling
%X Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length constraints, the ability to control the summary length in abstractive summarization is an important problem. In this paper, we propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. The results show that this approach generates high-quality summaries with user defined length, and outperforms the baselines consistently in terms of ROUGE score, length variations and semantic similarity.
%R 10.18653/v1/D18-1444
%U https://aclanthology.org/D18-1444
%U https://doi.org/10.18653/v1/D18-1444
%P 4110-4119

Markdown (Informal)

[Controlling Length in Abstractive Summarization Using a Convolutional Neural Network](https://aclanthology.org/D18-1444) (Liu et al., EMNLP 2018)

• Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (Liu et al., EMNLP 2018)

ACL

• Yizhu Liu, Zhiyi Luo, and Kenny Zhu. 2018. Controlling Length in Abstractive Summarization Using a Convolutional Neural Network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4110–4119, Brussels, Belgium. Association for Computational Linguistics.