RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910472927707136 |
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| author | Wu, Jinge Hasan, Abul Wu, Honghan |
| author_facet | Wu, Jinge Hasan, Abul Wu, Honghan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization Wu, Jinge Hasan, Abul Wu, Honghan Computation and Language 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. |
| title | RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.03062 |