KPDrop: An Approach to Improving Absent Keyphrase Generation

• URL: http://arxiv.org/abs/2112.01476v1
• Date: Thu, 2 Dec 2021 18:25:56 GMT
• Title: KPDrop: An Approach to Improving Absent Keyphrase Generation
• Authors: Seoyeon Park, Jishnu Ray Chowdhury, Tuhin Kundu, Cornelia Caragea
• Abstract summary: Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. We propose an approach, called keyphrase dropout (or KPDrop) to improve absent keyphrase generation.
• Score: 26.563045686728135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. The generated keyphrases can be either present or absent from the text of the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is very challenging; even the best methods show only a modest degree of success. In this paper, we propose an approach, called keyphrase dropout (or KPDrop), to improve absent keyphrase generation. We randomly drop present keyphrases from the document and turn them into artificial absent keyphrases during training. We test our approach extensively and show that it consistently improves the absent performance of strong baselines in keyphrase generation.

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