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    <titleInfo>
        <title>Using Human Attention to Extract Keyphrase from Microblog Post</title>
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        <namePart type="given">Yingyi</namePart>
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        <namePart type="given">Chengzhi</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a crucial element of human reading behavior. It reveals the relevance of words to the main topics of the target text. Thus, this paper aims to integrate human attention into keyphrase extraction models. First, human attention is represented by the reading duration estimated from eye-tracking corpus. Then, we merge human attention with neural network models by an attention mechanism. In addition, we also integrate human attention into unsupervised models. To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks. The experimental results show that our models have significant improvements on two Twitter datasets.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1588</identifier>
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%0 Conference Proceedings
%T Using Human Attention to Extract Keyphrase from Microblog Post
%A Zhang, Yingyi
%A Zhang, Chengzhi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-zhang-2019-using
%X This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a crucial element of human reading behavior. It reveals the relevance of words to the main topics of the target text. Thus, this paper aims to integrate human attention into keyphrase extraction models. First, human attention is represented by the reading duration estimated from eye-tracking corpus. Then, we merge human attention with neural network models by an attention mechanism. In addition, we also integrate human attention into unsupervised models. To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks. The experimental results show that our models have significant improvements on two Twitter datasets.
%R 10.18653/v1/P19-1588
%U https://aclanthology.org/P19-1588
%U https://doi.org/10.18653/v1/P19-1588
%P 5867-5872

Markdown (Informal)

[Using Human Attention to Extract Keyphrase from Microblog Post](https://aclanthology.org/P19-1588) (Zhang & Zhang, ACL 2019)

• Using Human Attention to Extract Keyphrase from Microblog Post (Zhang & Zhang, ACL 2019)

ACL

• Yingyi Zhang and Chengzhi Zhang. 2019. Using Human Attention to Extract Keyphrase from Microblog Post. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5867–5872, Florence, Italy. Association for Computational Linguistics.