Wu, J;
Shi, D;
Hasan, A;
Wu, H;
(2023)
KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization.
In:
Proceedings of the Annual Meeting of the Association for Computational Linguistics.
(pp. pp. 535-540).
ACL: Toronto, Canada.
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Abstract
This paper presents our contribution to the RadSum23 shared task organized as part of the BioNLP 2023. We compared state-of-the-art generative language models in generating high-quality summaries from radiology reports. A two-stage fine-tuning approach was introduced for utilizing knowledge learnt from different datasets. We evaluated the performance of our method using a variety of metrics, including BLEU, ROUGE, Bertscore, CheXbert, and RadGraph. Our results revealed the potentials of different models in summarizing radiology reports and demonstrated the effectiveness of the two-stage fine-tuning approach. We also discussed the limitations and future directions of our work, highlighting the need for better understanding the architecture design’s effect and optimal way of fine-tuning accordingly in automatic clinical summarizations.
| Type: | Proceedings paper |
|---|---|
| Title: | KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization |
| Event: | The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks |
| Dates: | Jul 2023 - Jul 2023 |
| ISBN-13: | 9781959429852 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/2023.bionlp-1.54 |
| Publisher version: | http://dx.doi.org/10.18653/v1/2023.bionlp-1.54 |
| Language: | English |
| Additional information: | ACL materials are Copyright © 1963–2023 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10181862 |
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