A Survey of Automatic Text Summarization Using Graph Neural Networks

Marco Ferdinand Salchner, Adam Jatowt


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
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6139–6150
Language:
URL:
https://aclanthology.org/2022.coling-1.536
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.536.pdf