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。

通过拆分和洗牌对 BART 进行 Feintuning 以增强关键词生成

Enhancing Keyphrase Generation by BART Finetuning with Splitting and  Shuffling

Keyphrase generation is the task of generating phrases (keyphrases) that
summarize the main topics of a given document. Keyphrases can be either present
or absent from the given document. While the extraction of present keyphrases
has received much attention in the past, only recently a stronger focus has
been placed on the generation of absent keyphrases. However, generating absent
keyphrases is challenging; even the best methods show only a modest degree of
success. In this paper, we propose a model-agnostic approach called keyphrase
dropout (or KPDrop) to improve absent keyphrase generation. In this approach,
we randomly drop present keyphrases from the document and turn them into
artificial absent keyphrases during training. We test our approach extensively
and show that it consistently improves the absent performance of strong
baselines in both supervised and resource-constrained semi-supervised settings.

本文提出了一种名为 KPDrop 的模型无关方法来提高缺失关键短语的生成，测试结果表明它可以在监督和资源受限的半监督设置中持续改善强基线的缺失性能。

KPDrop：提升缺失关键词生成

KPDrop: Improving Absent Keyphrase Generation

Neural keyphrase generation models have recently attracted much interest due
to their ability to output absent keyphrases, that is, keyphrases that do not
appear in the source text. In this paper, we discuss the usefulness of absent
keyphrases from an Information Retrieval (IR) perspective, and show that the
commonly drawn distinction between present and absent keyphrases is not made
explicit enough. We introduce a finer-grained categorization scheme that sheds
more light on the impact of absent keyphrases on scientific document retrieval.
Under this scheme, we find that only a fraction (around 20%) of the words that
make up keyphrases actually serves as document expansion, but that this small
fraction of words is behind much of the gains observed in retrieval
effectiveness. We also discuss how the proposed scheme can offer a new angle to
evaluate the output of neural keyphrase generation models.

本文讨论了神经关键词生成模型的实用性及其对信息检索的影响，引入了一种细分的分类方案，证明只有关键词中的很少一部分起到了文档扩展的作用，但这一小部分却是检索有效性提高的主要原因。同时，提出的方案可以作为一种新的角度来评估神经关键词生成模型的输出。