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.- Anthology ID:
- 2024.findings-naacl.102
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1568–1584
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.102
- DOI:
- Cite (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.
- Cite (Informal):
- Improving Absent Keyphrase Generation with Diversity Heads (Thomas & Vajjala, Findings 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.findings-naacl.102.pdf