Structure-Augmented Keyphrase Generation

Abstract

© 2021 Association for Computational LinguisticsThis paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (e.g., tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then encoding these information, using existing keyphrases of other documents, complementing missing/incomplete titles. Specifically, we first extend the given document with related but absent keyphrases from existing keyphrases, to augment missing contexts (generating structure), and then, build a graph of keyphrases and the given document, to obtain structure-aware representation of the augmented text (encoding structure). Our empirical results validate that our proposed structure augmentation and structure-aware encoding can improve KG for both scenarios, outperforming the state-of-the-art.