Efficient keyphrase generation with gans

Keyphrase Generation is the task of predicting keyphrases: short text sequences that convey the main semantic meaning of a document. In this paper, we introduce a keyphrase generation approach that makes use of a Generative Adversarial Networks (GANs) architecture. In our system, the Generator produces a sequence of keyphrases for an input document. The Discriminator, in turn, tries to distinguish between machine generated and human curated keyphrases. We propose a novel Discriminator architecture based on a BERT pretrained model f ine-tuned for Sequence Classification. We train our proposed architecture using only a small subset of the standard available training dataset, amounting to less than 1% of the total, achieving a great level of data efficiency. The resulting model is evaluated on five public datasets, obtaining competitive and promising results with respect to four state-of-the-art generative models.