Keyphrase Generation for Scientific Articles using GANs

TL;DR

This paper introduces a GAN-based method for generating diverse and accurate keyphrases for scientific articles, achieving state-of-the-art results on benchmark datasets.

Contribution

It presents a novel GAN framework for keyphrase generation that outperforms existing methods in accuracy and diversity.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Generates more diverse keyphrases than previous methods.

Comparable to top extractive techniques in quality.

Abstract

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.