Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues
Amal Alqahtani, Rana Salama, Mona Diab, Abdou Youssef
The 5th Workshop on Clinical Natural Language Processing (ClinicalNLP) N/a Paper
TLDR:
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
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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 and
BLEURT. Among the fine-tuned models, Flan-T5 achieved the highest aggregated score for dialogue summarization.