RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization

• URL: http://arxiv.org/abs/2406.03062v1
• Date: Wed, 5 Jun 2024 08:43:11 GMT
• Title: RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization
• Authors: Jinge Wu, Abul Hasan, Honghan Wu,
• Abstract summary: This study proposes RadBARTsum, a domain-specific and facilitated adaptation of the BART model for abstractive radiology report summarization. The approach involves two main steps: 1) re-training the BART model on a large corpus of radiology reports using a novel entity masking strategy to improve biomedical domain knowledge learning, and 2) fine-tuning the model for the summarization task using the Findings and Background sections to predict the Impression section.
• Score: 1.8450534779202723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiology report summarization is a crucial task that can help doctors quickly identify clinically significant findings without the need to review detailed sections of reports. This study proposes RadBARTsum, a domain-specific and ontology facilitated adaptation of the BART model for abstractive radiology report summarization. The approach involves two main steps: 1) re-training the BART model on a large corpus of radiology reports using a novel entity masking strategy to improving biomedical domain knowledge learning, and 2) fine-tuning the model for the summarization task using the Findings and Background sections to predict the Impression section. Experiments are conducted using different masking strategies. Results show that the re-training process with domain knowledge facilitated masking improves performances consistently across various settings. This work contributes a domain-specific generative language model for radiology report summarization and a method for utilising medical knowledge to realise entity masking language model. The proposed approach demonstrates a promising direction of enhancing the efficiency of language models by deepening its understanding of clinical knowledge in radiology reports.

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