  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.
