Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art (SOTA) methods select candidate keyphrases based on the similarity between learned representations of the candidates and the document. They suffer performance degradation on long documents due to discrepancy between sequence lengths which causes mismatch between representations of keyphrase candidates and the document. In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document. We further develop a KPE-oriented BERT (KPEBERT) model by proposing a novel self-supervised contrastive learning method, which is more compatible to MDERank than vanilla BERT. Comprehensive evaluations on six KPE benchmarks demonstrate that the proposed MDERank outperforms state-of-the-art unsupervised KPE approach by average 1.80 F 1@15 improvement. MDERank further benefits from KPEBERT and overall achieves average 3.53 F 1@15 improvement over the SOTA SIFRank. Our code is available at https: //github.com/LinhanZ/mderank.
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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