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        <title>Semi-Supervised Learning for Neural Keyphrase Generation</title>
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    <name type="personal">
        <namePart type="given">Hai</namePart>
        <namePart type="family">Ye</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a self-learning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1447</identifier>
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        <date>2018-oct-nov</date>
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            <start>4142</start>
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%0 Conference Proceedings
%T Semi-Supervised Learning for Neural Keyphrase Generation
%A Ye, Hai
%A Wang, Lu
%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 ye-wang-2018-semi
%X We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a self-learning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.
%R 10.18653/v1/D18-1447
%U https://aclanthology.org/D18-1447
%U https://doi.org/10.18653/v1/D18-1447
%P 4142-4153

Markdown (Informal)

[Semi-Supervised Learning for Neural Keyphrase Generation](https://aclanthology.org/D18-1447) (Ye & Wang, EMNLP 2018)

• Semi-Supervised Learning for Neural Keyphrase Generation (Ye & Wang, EMNLP 2018)

ACL

• Hai Ye and Lu Wang. 2018. Semi-Supervised Learning for Neural Keyphrase Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4142–4153, Brussels, Belgium. Association for Computational Linguistics.