Enhancing Keyphrase Generation by BART Finetuning with Splitting and
  Shuffling
Bin Chen Mizuho Iwaihara
Keyphrase generation is a task of identifying a set of phrases that best
repre-sent the main topics or themes of a given text. Keyphrases are dividend
int pre-sent and absent keyphrases. Recent approaches utilizing
sequence-to-sequence models show effectiveness on absent keyphrase generation.
However, the per-formance is still limited due to the hardness of finding
absent keyphrases. In this paper, we propose Keyphrase-Focused BART, which
exploits the differ-ences between present and absent keyphrase generations, and
performs fine-tuning of two separate BART models for present and absent
keyphrases. We further show effective approaches of shuffling keyphrases and
candidate keyphrase ranking. For absent keyphrases, our Keyphrase-Focused BART
achieved new state-of-the-art score on F1@5 in two out of five keyphrase
gen-eration benchmark datasets.