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    <titleInfo>
        <title>AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions</title>
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        <namePart type="given">Haoran</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank calculates self-attention and cross-attention using a pre-trained language model. The self-attention is designed to determine the importance of a candidate within the context of a sentence. The cross-attention is calculated to identify the semantic relevance between a candidate and sentences within a document. We evaluate the AttentionRank on three publicly available datasets against seven baselines. The results show that the AttentionRank is an effective and robust unsupervised keyphrase extraction model on both long and short documents. Source code is available on Github.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.146</identifier>
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    <part>
        <date>2021-11</date>
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            <start>1919</start>
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%0 Conference Proceedings
%T AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions
%A Ding, Haoran
%A Luo, Xiao
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ding-luo-2021-attentionrank
%X Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank calculates self-attention and cross-attention using a pre-trained language model. The self-attention is designed to determine the importance of a candidate within the context of a sentence. The cross-attention is calculated to identify the semantic relevance between a candidate and sentences within a document. We evaluate the AttentionRank on three publicly available datasets against seven baselines. The results show that the AttentionRank is an effective and robust unsupervised keyphrase extraction model on both long and short documents. Source code is available on Github.
%R 10.18653/v1/2021.emnlp-main.146
%U https://aclanthology.org/2021.emnlp-main.146
%U https://doi.org/10.18653/v1/2021.emnlp-main.146
%P 1919-1928

Markdown (Informal)

[AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions](https://aclanthology.org/2021.emnlp-main.146) (Ding & Luo, EMNLP 2021)

• AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (Ding & Luo, EMNLP 2021)

ACL

• Haoran Ding and Xiao Luo. 2021. AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1919–1928, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.