Importance Estimation from Multiple Perspectives for Keyphrase Extraction

Mingyang Song, Liping Jing, Lin Xiao


Abstract
Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation. From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, information saliency, and concept consistency simultaneously. However, most existing keyphrase extraction approaches only focus on the part of them, which leads to biased results. In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called as KIEMP) and further improve the performance of keyphrase extraction. Specifically, KIEMP estimates the importance of phrase with three modules: a chunking module to measure its syntactic accuracy, a ranking module to check its information saliency, and a matching module to judge the concept (i.e., topic) consistency between phrase and the whole document. These three modules are seamlessly jointed together via an end-to-end multi-task learning model, which is helpful for three parts to enhance each other and balance the effects of three perspectives. Experimental results on six benchmark datasets show that KIEMP outperforms the existing state-of-the-art keyphrase extraction approaches in most cases.
Anthology ID:
2021.emnlp-main.215
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2726–2736
Language:
URL:
https://aclanthology.org/2021.emnlp-main.215
DOI:
10.18653/v1/2021.emnlp-main.215
Bibkey:
Cite (ACL):
Mingyang Song, Liping Jing, and Lin Xiao. 2021. Importance Estimation from Multiple Perspectives for Keyphrase Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2726–2736, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Importance Estimation from Multiple Perspectives for Keyphrase Extraction (Song et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.215.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.215.mp4
Data
KP20k