Abstract
Keyphrase Extraction (KE) is a critical component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the document. Typically, a keyphrase extraction system can significantly accelerate the speed of information retrieval and help people get first-hand information from a long document quickly and accurately. Specifically, keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. In this paper, we introduce keyphrase extraction, present a review of the recent studies based on pre-trained language models, offer interesting insights on the different approaches, highlight open issues, and give a comparative experimental study of popular supervised as well as unsupervised techniques on several datasets. To encourage more instantiations, we release the related files mentioned in this paper.- Anthology ID:
- 2023.findings-eacl.161
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2153–2164
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.161
- DOI:
- Cite (ACL):
- Mingyang Song, Yi Feng, and Liping Jing. 2023. A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2153–2164, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (Song et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.findings-eacl.161.pdf