@inproceedings{swaminathan-etal-2020-preliminary,
title = "A Preliminary Exploration of {GAN}s for Keyphrase Generation",
author = "Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.645",
doi = "10.18653/v1/2020.emnlp-main.645",
pages = "8021--8030",
abstract = "We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques. Although we achieve promising results using GANs, they are not significantly better than the state-of-the-art generative models. To our knowledge, this is one of the first works that use GANs for keyphrase generation. We present a detailed analysis of our observations and expect that these findings would help other researchers to further study the use of GANs for the task of keyphrase generation.",
}
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%0 Conference Proceedings
%T A Preliminary Exploration of GANs for Keyphrase Generation
%A Swaminathan, Avinash
%A Zhang, Haimin
%A Mahata, Debanjan
%A Gosangi, Rakesh
%A Shah, Rajiv Ratn
%A Stent, Amanda
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F swaminathan-etal-2020-preliminary
%X We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques. Although we achieve promising results using GANs, they are not significantly better than the state-of-the-art generative models. To our knowledge, this is one of the first works that use GANs for keyphrase generation. We present a detailed analysis of our observations and expect that these findings would help other researchers to further study the use of GANs for the task of keyphrase generation.
%R 10.18653/v1/2020.emnlp-main.645
%U https://aclanthology.org/2020.emnlp-main.645
%U https://doi.org/10.18653/v1/2020.emnlp-main.645
%P 8021-8030
Markdown (Informal)
[A Preliminary Exploration of GANs for Keyphrase Generation](https://aclanthology.org/2020.emnlp-main.645) (Swaminathan et al., EMNLP 2020)
ACL
- Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, and Amanda Stent. 2020. A Preliminary Exploration of GANs for Keyphrase Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8021–8030, Online. Association for Computational Linguistics.