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        <title>Improving Absent Keyphrase Generation with Diversity Heads</title>
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            <namePart type="given">Kevin</namePart>
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    <abstract>Keyphrase Generation (KPG) is the task of automatically generating appropriate keyphrases for a given text, with a wide range of real-world applications such as document indexing and tagging, information retrieval, and text summarization. NLP research makes a distinction between present and absent keyphrases based on whether a keyphrase is directly present as a sequence of words in the document during evaluation. However, present and absent keyphrases are treated together in a text-to-text generation framework during training. We treat present keyphrase extraction as a sequence labeling problem and propose a new absent keyphrase generation model that uses a modified cross-attention layer with additional heads to capture diverse views for the same context encoding in this paper. Our experiments show improvements over the state-of-the-art for four datasets for present keyphrase extraction and five datasets for absent keyphrase generation among the six English datasets we explored, covering long and short documents.</abstract>
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%0 Conference Proceedings
%T Improving Absent Keyphrase Generation with Diversity Heads
%A Thomas, Edwin
%A Vajjala, Sowmya
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F thomas-vajjala-2024-improving
%X Keyphrase Generation (KPG) is the task of automatically generating appropriate keyphrases for a given text, with a wide range of real-world applications such as document indexing and tagging, information retrieval, and text summarization. NLP research makes a distinction between present and absent keyphrases based on whether a keyphrase is directly present as a sequence of words in the document during evaluation. However, present and absent keyphrases are treated together in a text-to-text generation framework during training. We treat present keyphrase extraction as a sequence labeling problem and propose a new absent keyphrase generation model that uses a modified cross-attention layer with additional heads to capture diverse views for the same context encoding in this paper. Our experiments show improvements over the state-of-the-art for four datasets for present keyphrase extraction and five datasets for absent keyphrase generation among the six English datasets we explored, covering long and short documents.
%R 10.18653/v1/2024.findings-naacl.102
%U https://aclanthology.org/2024.findings-naacl.102
%U https://doi.org/10.18653/v1/2024.findings-naacl.102
%P 1568-1584

Markdown (Informal)

[Improving Absent Keyphrase Generation with Diversity Heads](https://aclanthology.org/2024.findings-naacl.102) (Thomas & Vajjala, Findings 2024)

• Improving Absent Keyphrase Generation with Diversity Heads (Thomas & Vajjala, Findings 2024)

ACL

• Edwin Thomas and Sowmya Vajjala. 2024. Improving Absent Keyphrase Generation with Diversity Heads. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1568–1584, Mexico City, Mexico. Association for Computational Linguistics.