  Recently, generative methods have been widely used in keyphrase prediction,
thanks to their capability to produce both present keyphrases that appear in
the source text and absent keyphrases that do not match any source text.
However, the absent keyphrases are generated at the cost of the performance on
present keyphrase prediction, since previous works mainly use generative models
that rely on the copying mechanism and select words step by step. Besides, the
extractive model that directly extracts a text span is more suitable for
predicting the present keyphrase. Considering the different characteristics of
extractive and generative methods, we propose to divide the keyphrase
prediction into two subtasks, i.e., present keyphrase extraction (PKE) and
absent keyphrase generation (AKG), to fully exploit their respective
advantages. On this basis, a joint inference framework is proposed to make the
most of BERT in two subtasks. For PKE, we tackle this task as a sequence
labeling problem with the pre-trained language model BERT. For AKG, we
introduce a Transformer-based architecture, which fully integrates the present
keyphrase knowledge learned from PKE by the fine-tuned BERT. The experimental
results show that our approach can achieve state-of-the-art results on both
tasks on benchmark datasets.
