@inproceedings{gonzalez-sanchez-martinez-2024-hulat,
title = "{HULAT}-{UC}3{M} at {B}iolay{S}umm: Adaptation of {B}io{BART} and Longformer models to summarizing biomedical documents",
author = "Gonzalez Sanchez, Adrian and
Mart{\'\i}nez, Paloma",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.71",
doi = "10.18653/v1/2024.bionlp-1.71",
pages = "780--785",
abstract = "This article presents our submission to the Bio- LaySumm 2024 shared task: Lay Summarization of Biomedical Research Articles. The objective of this task is to generate summaries that are simplified in a concise and less technical way, in order to facilitate comprehension by non-experts users. A pre-trained BioBART model was employed to fine-tune the articles from the two journals, thereby generating two models, one for each journal. The submission achieved the 12th best ranking in the task, attaining a meritorious first place in the Relevance ROUGE-1 metric.",
}
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%0 Conference Proceedings
%T HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents
%A Gonzalez Sanchez, Adrian
%A Martínez, Paloma
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F gonzalez-sanchez-martinez-2024-hulat
%X This article presents our submission to the Bio- LaySumm 2024 shared task: Lay Summarization of Biomedical Research Articles. The objective of this task is to generate summaries that are simplified in a concise and less technical way, in order to facilitate comprehension by non-experts users. A pre-trained BioBART model was employed to fine-tune the articles from the two journals, thereby generating two models, one for each journal. The submission achieved the 12th best ranking in the task, attaining a meritorious first place in the Relevance ROUGE-1 metric.
%R 10.18653/v1/2024.bionlp-1.71
%U https://aclanthology.org/2024.bionlp-1.71
%U https://doi.org/10.18653/v1/2024.bionlp-1.71
%P 780-785
Markdown (Informal)
[HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents](https://aclanthology.org/2024.bionlp-1.71) (Gonzalez Sanchez & Martínez, BioNLP-WS 2024)
ACL