Abstract
Although automatic text summarization (ATS) has been researched for several decades, the application of graph neural networks (GNNs) to this task started relatively recently. In this survey we provide an overview on the rapidly evolving approach of using GNNs for the task of automatic text summarization. In particular we provide detailed information on the functionality of GNNs in the context of ATS, and a comprehensive overview of models utilizing this approach.- Anthology ID:
- 2022.coling-1.536
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6139–6150
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.536
- DOI:
- Cite (ACL):
- Marco Ferdinand Salchner and Adam Jatowt. 2022. A Survey of Automatic Text Summarization Using Graph Neural Networks. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6139–6150, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A Survey of Automatic Text Summarization Using Graph Neural Networks (Salchner & Jatowt, COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.coling-1.536.pdf