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.- Anthology ID:
- 2023.bionlp-1.54
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 535–540
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.54
- DOI:
- 10.18653/v1/2023.bionlp-1.54
- Cite (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.
- Cite (Informal):
- KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization (Wu et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.bionlp-1.54.pdf