@inproceedings{kim-etal-2024-saama-technologies,
title = "Saama Technologies at {B}io{L}ay{S}umm: Abstract based fine-tuned models with {L}o{RA}",
author = "Kim, Hwanmun and
Kanakarajan, Kamal raj and
Sankarasubbu, Malaikannan",
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-1.72",
doi = "10.18653/v1/2024.bionlp-1.72",
pages = "786--792",
abstract = "Lay summarization of biomedical research articles is a challenging problem due to their use of technical terms and background knowledge requirements, despite the potential benefits of these research articles to the public. We worked on this problem as participating in BioLaySumm 2024. We experimented with various fine-tuning approaches to generate better lay summaries for biomedical research articles. After several experiments, we built a LoRA model with unsupervised fine-tuning based on the abstracts of the given articles, followed by a post-processing unit to take off repeated sentences. Our model was ranked 3rd overall in the BioLaySumm 2024 leaderboard. We analyzed the different approaches we experimented with and suggested several ideas to improve our model further.",
}
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<abstract>Lay summarization of biomedical research articles is a challenging problem due to their use of technical terms and background knowledge requirements, despite the potential benefits of these research articles to the public. We worked on this problem as participating in BioLaySumm 2024. We experimented with various fine-tuning approaches to generate better lay summaries for biomedical research articles. After several experiments, we built a LoRA model with unsupervised fine-tuning based on the abstracts of the given articles, followed by a post-processing unit to take off repeated sentences. Our model was ranked 3rd overall in the BioLaySumm 2024 leaderboard. We analyzed the different approaches we experimented with and suggested several ideas to improve our model further.</abstract>
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%0 Conference Proceedings
%T Saama Technologies at BioLaySumm: Abstract based fine-tuned models with LoRA
%A Kim, Hwanmun
%A Kanakarajan, Kamal raj
%A Sankarasubbu, Malaikannan
%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 kim-etal-2024-saama-technologies
%X Lay summarization of biomedical research articles is a challenging problem due to their use of technical terms and background knowledge requirements, despite the potential benefits of these research articles to the public. We worked on this problem as participating in BioLaySumm 2024. We experimented with various fine-tuning approaches to generate better lay summaries for biomedical research articles. After several experiments, we built a LoRA model with unsupervised fine-tuning based on the abstracts of the given articles, followed by a post-processing unit to take off repeated sentences. Our model was ranked 3rd overall in the BioLaySumm 2024 leaderboard. We analyzed the different approaches we experimented with and suggested several ideas to improve our model further.
%R 10.18653/v1/2024.bionlp-1.72
%U https://aclanthology.org/2024.bionlp-1.72
%U https://doi.org/10.18653/v1/2024.bionlp-1.72
%P 786-792
Markdown (Informal)
[Saama Technologies at BioLaySumm: Abstract based fine-tuned models with LoRA](https://aclanthology.org/2024.bionlp-1.72) (Kim et al., BioNLP-WS 2024)
ACL