A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models
Mingyang Song, Yi Feng, Liping Jing
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.