  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.
