Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning

Ravi Kondadadi, Sahil Manchanda, Jason Ngo, Ronan McCormack


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
Venues:
BioNLP | NAACL
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.bionlp-1.32.pdf