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.

提出了 Keyphrase-Focused BART 方法来处理关键短语生成任务，利用序列到序列模型在存在和不存在关键短语生成上的差异进行细微调整，并展示了洗牌关键短语和候选关键短语排名的有效方法，在五个关键短语生成基准数据集中，我们的 Keyphrase-Focused BART 在不存在关键短语上达到了最新的 F1@5 最高得分。