Keyphrase Generation for Scientific Articles using GANs
This addresses the problem of automatically generating keyphrases for scientific articles, which is an incremental improvement in natural language processing for document indexing and retrieval.
The paper tackles keyphrase generation for scientific articles by using conditional Generative Adversarial Networks (GANs), achieving state-of-the-art performance in abstractive keyphrase generation and comparable results to extractive techniques.
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.