Keyphrase Generation for Scientific Articles using GANs

Autor:	Swaminathan, Avinash, Gupta, Raj Kuwar, Zhang, Haimin, Mahata, Debanjan, Gosangi, Rakesh, Shah, Rajiv Ratn
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated keyphrases. We evaluate this approach on standard benchmark datasets. Our model achieves state-of-the-art performance in generation of abstractive keyphrases and is also comparable to the best performing extractive techniques. We also demonstrate that our method generates more diverse keyphrases and make our implementation publicly available. Comment: 2 pages, 1 fig, 8 references, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.12229 Zobrazit plný text záznamu View this record from Arxiv