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Improving absent keyphrase generation with diversity heads

From National Research Council Canada

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Linkhttps://aclanthology.org/2024.findings-naacl.102
AuthorSearch for: ; Search for: 1
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Conference2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), June 16-21, 2024, Mexico City, Mexico
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.
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PublisherAssociation for Computational Linguistics
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In
  • Findings of the Association for Computational Linguistics: NAACL 2024, Mexico City, Mexico.: 1568–1584.
LanguageEnglish
Peer reviewedYes
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Record identifierc4177569-2763-4c66-9e89-d598d2e02cb6
Record created2024-07-19
Record modified2024-07-19
Date modified: