Recent years have seen a flourishing of neural keyphrase generation  works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on keyphrase generation tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among models, and an investigation on related factors (e.g., architectural choice, decoding strategy) that may affect a keyphrase generation system's  performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the performance of keyphrase generation models. We hope this study can help clarify some of the uncertainties surrounding the keyphrase generation task and facilitate future research on this topic.
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.
A
(KPG)
R
keyphrase generation
KPG
R
models, and an
different model designs, and a thorough
D
(e.g., architectural choice, decoding strategy)
R
keyphrase generation
KPG
A
generalization
R
performance of keyphrase generation
generalizability of KPG
R
keyphrase generation
KPG