@inproceedings{obonyo-etal-2022-exploring,
title = "Exploring the limits of a base {BART} for multi-document summarization in the medical domain",
author = "Obonyo, Ishmael and
Casola, Silvia and
Saggion, Horacio",
editor = "Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Lucy Lu",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.23/",
pages = "193--198",
abstract = "This paper is a description of our participation in the Multi-document Summarization for Literature Review (MSLR) Shared Task, in which we explore summarization models to create an automatic review of scientific results. Rather than maximizing the metrics using expensive computational models, we placed ourselves in a situation of scarce computational resources and explore the limits of a base sequence to sequence models (thus with a limited input length) to the task. Although we explore methods to feed the abstractive model with salient sentences only (using a first extractive step), we find the results still need some improvements."
}
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%0 Conference Proceedings
%T Exploring the limits of a base BART for multi-document summarization in the medical domain
%A Obonyo, Ishmael
%A Casola, Silvia
%A Saggion, Horacio
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Lucy Lu
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F obonyo-etal-2022-exploring
%X This paper is a description of our participation in the Multi-document Summarization for Literature Review (MSLR) Shared Task, in which we explore summarization models to create an automatic review of scientific results. Rather than maximizing the metrics using expensive computational models, we placed ourselves in a situation of scarce computational resources and explore the limits of a base sequence to sequence models (thus with a limited input length) to the task. Although we explore methods to feed the abstractive model with salient sentences only (using a first extractive step), we find the results still need some improvements.
%U https://aclanthology.org/2022.sdp-1.23/
%P 193-198
Markdown (Informal)
[Exploring the limits of a base BART for multi-document summarization in the medical domain](https://aclanthology.org/2022.sdp-1.23/) (Obonyo et al., sdp 2022)
ACL