SimCKP: Simple Contrastive Learning of Keyphrase Representations
Minseok Choi Chaeheon Gwak Seho Kim Si Hyeong Kim Jaegul Choo
Keyphrase generation (KG) aims to generate a set of summarizing words or
phrases given a source document, while keyphrase extraction (KE) aims to
identify them from the text. Because the search space is much smaller in KE, it
is often combined with KG to predict keyphrases that may or may not exist in
the corresponding document. However, current unified approaches adopt sequence
labeling and maximization-based generation that primarily operate at a token
level, falling short in observing and scoring keyphrases as a whole. In this
work, we propose SimCKP, a simple contrastive learning framework that consists
of two stages: 1) An extractor-generator that extracts keyphrases by learning
context-aware phrase-level representations in a contrastive manner while also
generating keyphrases that do not appear in the document; 2) A reranker that
adapts scores for each generated phrase by likewise aligning their
representations with the corresponding document. Experimental results on
multiple benchmark datasets demonstrate the effectiveness of our proposed
approach, which outperforms the state-of-the-art models by a significant
margin.