Keyphrase Generation: A Multi-Aspect Survey

Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequence-to-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.Comment: 10 pages, 5 tables. Published in proceedings of FRUCT 2019, the 25th Conference of the Open Innovations Association FRUCT, Helsinki, Finlan

Keyphrase Generation with GANs in Low-Resources Scenarios

Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document. Several past studies provided diverse approaches to generate Keyphrases for an input document. However, all of these approaches still need to be trained on very large datasets. In this paper, we introduce BeGan-KP, a new conditional GAN model to address the problem of Keyphrase Generation in a low-resource scenario. Our main contribution relies in the Discriminator\u2019s architecture: a new BERT-based module which is able to distinguish between the generated and humancurated KPs reliably. Its characteristics allow us to use it in a low-resource scenario, where only a small amount of training data are available, obtaining an efficient Generator. The resulting architecture achieves, on five public datasets, competitive results with respect to the state-of-the-art approaches, using less than 1% of the training data

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 fine-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