Abstract
Summarizing medical conversations is one of the tasks proposed by MEDIQA-Chat to promote research on automatic clinical note generation from doctor-patient conversations. In this paper, we present our submission to this task using fine-tuned language models, including T5, BART and BioGPT models. The fine-tuned models are evaluated using ensemble metrics including ROUGE, BERTScore andBLEURT. Among the fine-tuned models, Flan-T5 achieved the highest aggregated score for dialogue summarization.- Anthology ID:
- 2023.clinicalnlp-1.55
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 524–528
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.55
- DOI:
- Cite (ACL):
- Amal Alqahtani, Rana Salama, Mona Diab, and Abdou Youssef. 2023. Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 524–528, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues (Alqahtani et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2023.clinicalnlp-1.55.pdf