UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction

TL;DR

UniKeyphrase is an end-to-end framework that jointly extracts and generates keyphrases, leveraging semantic relations to improve prediction accuracy, and outperforms existing methods on benchmark datasets.

Contribution

It introduces a novel joint learning framework with relation modeling and constraints, enhancing keyphrase prediction performance.

Findings

Outperforms mainstream KP methods significantly.

Effectively captures semantic relations between extraction and generation.

Achieves state-of-the-art results on KP benchmarks.

Abstract

Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly. In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases. In UniKeyphrase, stacked relation layer and bag-of-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively. Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin.