MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction

• URL: http://arxiv.org/abs/2110.06651v1
• Date: Wed, 13 Oct 2021 11:29:17 GMT
• Title: MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
• Authors: Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, Shiliang Zhang, Bing Li, Wei Wang, Xin Cao
• Abstract summary: Keyphrases are phrases in a document providing a concise summary of core content, helping readers to understand what the article is talking about in a minute. We propose a novel unsupervised keyword extraction method by leveraging the BERT-based model to select and rank candidate keyphrases with a MASK strategy.
• Score: 41.941098507759015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Keyphrases are phrases in a document providing a concise summary of core content, helping readers to understand what the article is talking about in a minute. However, existing unsupervised works are not robust enough to handle various types of documents owing to the mismatch of sequence length for comparison. In this paper, we propose a novel unsupervised keyword extraction method by leveraging the BERT-based model to select and rank candidate keyphrases with a MASK strategy. In addition, we further enhance the model, denoted as Keyphrases Extraction BERT (KPEBERT), via designing a compatible self-supervised task and conducting a contrast learning. We conducted extensive experimental evaluation to demonstrate the superiority and robustness of the proposed method as well as the effectiveness of KPEBERT.

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