@inproceedings{ray-chowdhury-etal-2022-kpdrop,
title = "{KPDROP}: Improving Absent Keyphrase Generation",
author = "Ray Chowdhury, Jishnu and
Park, Seo Yeon and
Kundu, Tuhin and
Caragea, Cornelia",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.357",
doi = "10.18653/v1/2022.findings-emnlp.357",
pages = "4853--4870",
abstract = "Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from 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 challenging; even the best methods show only a modest degree of success. In this paper, we propose a model-agnostic approach called keyphrase dropout (or KPDrop) to improve absent keyphrase generation. In this approach, 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 both supervised and resource-constrained semi-supervised settings.",
}
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<abstract>Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from 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 challenging; even the best methods show only a modest degree of success. In this paper, we propose a model-agnostic approach called keyphrase dropout (or KPDrop) to improve absent keyphrase generation. In this approach, 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 both supervised and resource-constrained semi-supervised settings.</abstract>
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%0 Conference Proceedings
%T KPDROP: Improving Absent Keyphrase Generation
%A Ray Chowdhury, Jishnu
%A Park, Seo Yeon
%A Kundu, Tuhin
%A Caragea, Cornelia
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ray-chowdhury-etal-2022-kpdrop
%X Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from 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 challenging; even the best methods show only a modest degree of success. In this paper, we propose a model-agnostic approach called keyphrase dropout (or KPDrop) to improve absent keyphrase generation. In this approach, 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 both supervised and resource-constrained semi-supervised settings.
%R 10.18653/v1/2022.findings-emnlp.357
%U https://aclanthology.org/2022.findings-emnlp.357
%U https://doi.org/10.18653/v1/2022.findings-emnlp.357
%P 4853-4870
Markdown (Informal)
[KPDROP: Improving Absent Keyphrase Generation](https://aclanthology.org/2022.findings-emnlp.357) (Ray Chowdhury et al., Findings 2022)
ACL
- Jishnu Ray Chowdhury, Seo Yeon Park, Tuhin Kundu, and Cornelia Caragea. 2022. KPDROP: Improving Absent Keyphrase Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4853–4870, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.