Data Augmentation for Low-Resource Keyphrase Generation

Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but they still rely on a lot of additional unlabeled data for pretraining and on automatic methods for pseudo-annotations. In this paper, we present data augmentation strategies specifically to address keyphrase generation in purely resource-constrained domains. We design techniques that use the full text of the articles to improve both present and absent keyphrase generation. We test our approach comprehensively on three datasets and show that the data augmentation strategies consistently improve the state-of-the-art performance. We release our source code at https://github.com/kgarg8/kpgen-lowres-data-aug.

Keyphrase Generation Beyond the Boundaries of Title and Abstract

Keyphrase generation aims at generating phrases (keyphrases) that best describe a given document. In scholarly domains, current approaches to this task are neural approaches and have largely worked with only the title and abstract of the articles. In this work, we explore whether the integration of additional data from semantically similar articles or from the full text of the given article can be helpful for a neural keyphrase generation model. We discover that adding sentences from the full text particularly in the form of summary of the article can significantly improve the generation of both types of keyphrases that are either present or absent from the title and abstract. The experimental results on the three acclaimed models along with one of the latest transformer models suitable for longer documents, Longformer Encoder-Decoder (LED) validate the observation. We also present a new large-scale scholarly dataset FullTextKP for keyphrase generation, which we use for our experiments. Unlike prior large-scale datasets, FullTextKP includes the full text of the articles alongside title and abstract. We will release the source code to stimulate research on the proposed ideas.