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        <title>KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization</title>
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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.</abstract>
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%0 Conference Proceedings
%T KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization
%A Wu, Jinge
%A Shi, Daqian
%A Hasan, Abul
%A Wu, Honghan
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-knowlab
%X 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.
%R 10.18653/v1/2023.bionlp-1.54
%U https://aclanthology.org/2023.bionlp-1.54
%U https://doi.org/10.18653/v1/2023.bionlp-1.54
%P 535-540

Markdown (Informal)

[KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization](https://aclanthology.org/2023.bionlp-1.54) (Wu et al., BioNLP 2023)

• KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization (Wu et al., BioNLP 2023)

ACL

• Jinge Wu, Daqian Shi, Abul Hasan, and Honghan Wu. 2023. KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 535–540, Toronto, Canada. Association for Computational Linguistics.