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.

翻译：关键短语生成是一项将任意给定文章内容总结为若干突出短语（即关键短语）的任务。现有研究大多依赖大规模标注数据集，而这类数据难以获取。极少有工作探讨低资源场景下关键短语生成问题，但这些工作仍需大量额外无标签数据用于预训练，并依赖自动方法进行伪标注。本文专门针对资源受限领域的纯低资源关键短语生成问题，提出了数据增强策略。我们设计了利用文章全文来提升现有关键短语与缺失关键短语生成效果的技术。通过在三个数据集上的全面测试，我们证明了所提出的数据增强策略能够持续提升当前最优性能。我们已在 https://github.com/kgarg8/kpgen-lowres-data-aug 公开源代码。