@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.",
}
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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 andBLEURT. Among the fine-tuned models, Flan-T5 achieved the highest aggregated score for dialogue summarization.</abstract>
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%0 Conference Proceedings
%T Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues
%A Alqahtani, Amal
%A Salama, Rana
%A Diab, Mona
%A Youssef, Abdou
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F alqahtani-etal-2023-care4lang
%X 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.
%R 10.18653/v1/2023.clinicalnlp-1.55
%U https://aclanthology.org/2023.clinicalnlp-1.55
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.55
%P 524-528
Markdown (Informal)
[Care4Lang at MEDIQA-Chat 2023: Fine-tuning Language Models for Classifying and Summarizing Clinical Dialogues](https://aclanthology.org/2023.clinicalnlp-1.55) (Alqahtani et al., ClinicalNLP 2023)
ACL