Abstract
Keywords or keyphrases are often used to highlight a document’s domains or main topics. Unsupervised keyphrase extraction (UKE) has always been highly anticipated because no labeled data is needed to train a model. This paper proposes an augmented graph-based unsupervised model to identify keyphrases from a document by integrating graph and deep learning methods. The proposed model utilizes mutual attention extracted from the pre-trained BERT model to build the candidate graph and augments the graph with global and local context nodes to improve the performance. The proposed model is evaluated on four publicly available datasets against thirteen UKE baselines. The results show that the proposed model is an effective and robust UKE model for long and short documents. Our source code is available on GitHub.- Anthology ID:
- 2022.aacl-main.19
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Editors:
- Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 230–239
- Language:
- URL:
- https://aclanthology.org/2022.aacl-main.19
- DOI:
- Cite (ACL):
- Haoran Ding and Xiao Luo. 2022. AGRank: Augmented Graph-based Unsupervised Keyphrase Extraction. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 230–239, Online only. Association for Computational Linguistics.
- Cite (Informal):
- AGRank: Augmented Graph-based Unsupervised Keyphrase Extraction (Ding & Luo, AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.aacl-main.19.pdf