Unsupervised document summarization using pre-trained sentence embeddings and graph centrality

Juan Ramirez-Orta; Evangelos Milios

doi:10.18653/v1/2021.sdp-1.14

Unsupervised document summarization using pre-trained sentence embeddings and graph centrality

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Abstract

This paper describes our submission for the LongSumm task in SDP 2021. We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner. The proposed method is simple, fast, can summarize any kind of document of any size and can satisfy any length constraints for the summaries produced. The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques

Anthology ID:: 2021.sdp-1.14
Volume:: Proceedings of the Second Workshop on Scholarly Document Processing
Month:: June
Year:: 2021
Address:: Online
Editors:: Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Lu Wang
Venue:: sdp
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 110–115
Language:
URL:: https://aclanthology.org/2021.sdp-1.14/
DOI:: 10.18653/v1/2021.sdp-1.14
Bibkey:
Cite (ACL):: Juan Ramirez-Orta and Evangelos Milios. 2021. Unsupervised document summarization using pre-trained sentence embeddings and graph centrality. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 110–115, Online. Association for Computational Linguistics.
Cite (Informal):: Unsupervised document summarization using pre-trained sentence embeddings and graph centrality (Ramirez-Orta & Milios, sdp 2021)
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PDF:: https://aclanthology.org/2021.sdp-1.14.pdf

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@inproceedings{ramirez-orta-milios-2021-unsupervised,
    title = "Unsupervised document summarization using pre-trained sentence embeddings and graph centrality",
    author = "Ramirez-Orta, Juan  and
      Milios, Evangelos",
    editor = "Beltagy, Iz  and
      Cohan, Arman  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hall, Keith  and
      Herrmannova, Drahomira  and
      Knoth, Petr  and
      Lo, Kyle  and
      Mayr, Philipp  and
      Patton, Robert M.  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita  and
      Wang, Kuansan  and
      Wang, Lucy Lu",
    booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.sdp-1.14/",
    doi = "10.18653/v1/2021.sdp-1.14",
    pages = "110--115",
    abstract = "This paper describes our submission for the LongSumm task in SDP 2021. We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner. The proposed method is simple, fast, can summarize any kind of document of any size and can satisfy any length constraints for the summaries produced. The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques"
}

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%0 Conference Proceedings
%T Unsupervised document summarization using pre-trained sentence embeddings and graph centrality
%A Ramirez-Orta, Juan
%A Milios, Evangelos
%Y Beltagy, Iz
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hall, Keith
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Kuansan
%Y Wang, Lucy Lu
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ramirez-orta-milios-2021-unsupervised
%X This paper describes our submission for the LongSumm task in SDP 2021. We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner. The proposed method is simple, fast, can summarize any kind of document of any size and can satisfy any length constraints for the summaries produced. The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques
%R 10.18653/v1/2021.sdp-1.14
%U https://aclanthology.org/2021.sdp-1.14/
%U https://doi.org/10.18653/v1/2021.sdp-1.14
%P 110-115

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Markdown (Informal)

[Unsupervised document summarization using pre-trained sentence embeddings and graph centrality](https://aclanthology.org/2021.sdp-1.14/) (Ramirez-Orta & Milios, sdp 2021)

Unsupervised document summarization using pre-trained sentence embeddings and graph centrality (Ramirez-Orta & Milios, sdp 2021)

ACL

Juan Ramirez-Orta and Evangelos Milios. 2021. Unsupervised document summarization using pre-trained sentence embeddings and graph centrality. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 110–115, Online. Association for Computational Linguistics.