  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.
