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        <title>A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models
%A Song, Mingyang
%A Feng, Yi
%A Jing, Liping
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F song-etal-2023-survey
%X 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.
%R 10.18653/v1/2023.findings-eacl.161
%U https://aclanthology.org/2023.findings-eacl.161
%U https://doi.org/10.18653/v1/2023.findings-eacl.161
%P 2153-2164

Markdown (Informal)

[A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models](https://aclanthology.org/2023.findings-eacl.161) (Song et al., Findings 2023)

• A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (Song et al., Findings 2023)

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.