  Keyphrase extraction is the task of extracting a small set of phrases that
best describe a document. Most existing benchmark datasets for the task
typically have limited numbers of annotated documents, making it challenging to
train increasingly complex neural networks. In contrast, digital libraries
store millions of scientific articles online, covering a wide range of topics.
While a significant portion of these articles contain keyphrases provided by
their authors, most other articles lack such kind of annotations. Therefore, to
effectively utilize these large amounts of unlabeled articles, we propose a
simple and efficient joint learning approach based on the idea of
self-distillation. Experimental results show that our approach consistently
improves the performance of baseline models for keyphrase extraction.
Furthermore, our best models outperform previous methods for the task,
achieving new state-of-the-art results on two public benchmarks: Inspec and
SemEval-2017.
