A Survey of Automatic Text Summarization Using Graph Neural Networks

Marco Ferdinand Salchner; Adam Jatowt

A Survey of Automatic Text Summarization Using Graph Neural Networks

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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:
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)
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PDF:: https://aclanthology.org/2022.coling-1.536.pdf

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@inproceedings{salchner-jatowt-2022-survey,
    title = "A Survey of Automatic Text Summarization Using Graph Neural Networks",
    author = "Salchner, Marco Ferdinand  and
      Jatowt, Adam",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.536/",
    pages = "6139--6150",
    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."
}

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%0 Conference Proceedings
%T A Survey of Automatic Text Summarization Using Graph Neural Networks
%A Salchner, Marco Ferdinand
%A Jatowt, Adam
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F salchner-jatowt-2022-survey
%X 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.
%U https://aclanthology.org/2022.coling-1.536/
%P 6139-6150

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Markdown (Informal)

[A Survey of Automatic Text Summarization Using Graph Neural Networks](https://aclanthology.org/2022.coling-1.536/) (Salchner & Jatowt, COLING 2022)

A Survey of Automatic Text Summarization Using Graph Neural Networks (Salchner & Jatowt, COLING 2022)

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.