Abstract
This paper describes experiments undertaken and their results as part of the BioNLP MEDIQA 2021 challenge. We participated in Task 3: Radiology Report Summarization. Multiple runs were submitted for evaluation, from solutions leveraging transfer learning from pre-trained transformer models, which were then fine tuned on a subset of MIMIC-CXR, for abstractive report summarization. The task was evaluated using ROUGE and our best performing system obtained a ROUGE-2 score of 0.392.- Anthology ID:
- 2021.bionlp-1.32
- Volume:
- Proceedings of the 20th Workshop on Biomedical Language Processing
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 280–284
- Language:
- URL:
- https://aclanthology.org/2021.bionlp-1.32
- DOI:
- 10.18653/v1/2021.bionlp-1.32
- Cite (ACL):
- Ravi Kondadadi, Sahil Manchanda, Jason Ngo, and Ronan McCormack. 2021. Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 280–284, Online. Association for Computational Linguistics.
- Cite (Informal):
- Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning (Kondadadi et al., BioNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.bionlp-1.32.pdf