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Keyphrase extraction (KPE) automatically extracts phrases in a document thatprovide a concise summary of the core content, which benefits downstreaminformation retrieval and NLP tasks. Previous state-of-the-art (SOTA) methodsselect candidate keyphrases based on the similarity between learnedrepresentations of the candidates and the document. They suffer performancedegradation on long documents due to discrepancy between sequence lengths whichcauses mismatch between representations of keyphrase candidates and thedocument. In this work, we propose a novel unsupervised embedding-based KPEapproach, Masked Document Embedding Rank (MDERank), to address this problem byleveraging a mask strategy and ranking candidates by the similarity betweenembeddings of the source document and the masked document. We further develop aKPE-oriented BERT (KPEBERT) model by proposing a novel self-supervisedcontrastive learning method, which is more compatible to MDERank than vanillaBERT. Comprehensive evaluations on six KPE benchmarks demonstrate that theproposed MDERank outperforms state-of-the-art unsupervised KPE approach byaverage 1.80 $F1@15$ improvement. MDERank further benefits from KPEBERT andoverall achieves average 3.53 $F1@15$ improvement over the SOTA SIFRank. Ourcode is available at \url{https://github.com/LinhanZ/mderank}.

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