An Empirical Study on Neural Keyphrase Generation

Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, Daqing He


Abstract
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.
Anthology ID:
2021.naacl-main.396
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4985–5007
Language:
URL:
https://aclanthology.org/2021.naacl-main.396
DOI:
10.18653/v1/2021.naacl-main.396
Bibkey:
Cite (ACL):
Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, and Daqing He. 2021. An Empirical Study on Neural Keyphrase Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4985–5007, Online. Association for Computational Linguistics.
Cite (Informal):
An Empirical Study on Neural Keyphrase Generation (Meng et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.396.pdf
Video:
 https://aclanthology.org/2021.naacl-main.396.mp4
Data
KP20k