  This study addresses the application of encoder-only Pre-trained Language
Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of
domain-tailored encoder-only models compared to encoder-decoder models. We
investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG,
(2) optimal architectural decisions for employing encoder-only PLMs in KPG, and
(3) a performance comparison between in-domain encoder-only and encoder-decoder
PLMs across varied resource settings. Our findings, derived from extensive
experimentation in two domains reveal that with encoder-only PLMs, although KPE
with Conditional Random Fields slightly excels in identifying present
keyphrases, the KPG formulation renders a broader spectrum of keyphrase
predictions. Additionally, prefix-LM fine-tuning of encoder-only PLMs emerges
as a strong and data-efficient strategy for KPG, outperforming general-domain
seq2seq PLMs. We also identify a favorable parameter allocation towards model
depth rather than width when employing encoder-decoder architectures
initialized with encoder-only PLMs. The study sheds light on the potential of
utilizing encoder-only PLMs for advancing KPG systems and provides a groundwork
for future KPG methods. Our code and pre-trained checkpoints are released at
https://github.com/uclanlp/DeepKPG.
