@inproceedings{alqahtani-etal-2023-care4lang,
    title = "{C}are4{L}ang at {MEDIQA}-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues",
    author = "Alqahtani, Amal  and
      Salama, Rana  and
      Diab, Mona  and
      Youssef, Abdou",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.55",
    doi = "10.18653/v1/2023.clinicalnlp-1.55",
    pages = "524--528",
    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.",
}

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.
Alqahtani, Amal Salama, Rana Diab, Mona Youssef, Abdou
Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues