@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
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)
ACL