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        <title>In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations</title>
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    <abstract>Summarization of doctor-patient conversations into clinical notes by medical scribes is an essential process for effective clinical care. Pre-trained transformer models have shown a great amount of success in this area, but the domain shift from standard NLP tasks to the medical domain continues to present challenges. We build upon several recent works to show that additional pre-training with in-domain medical conversations leads to performance gains for clinical summarization. In addition to conventional evaluation metrics, we also explore a clinical named entity recognition model for concept-based evaluation. Finally, we contrast long-sequence transformers with a common transformer model, BART. Overall, our findings corroborate research in non-medical domains and suggest that in-domain pre-training combined with transformers for long sequences are effective strategies for summarizing clinical encounters.</abstract>
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%0 Conference Proceedings
%T In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations
%A Grambow, Colin
%A Zhang, Longxiang
%A Schaaf, Thomas
%Y Krahmer, Emiel
%Y McCoy, Kathy
%Y Reiter, Ehud
%S Proceedings of the First Workshop on Natural Language Generation in Healthcare
%D 2022
%8 July
%I Association for Computational Linguistics
%C Waterville, Maine, USA and virtual meeting
%F grambow-etal-2022-domain
%X Summarization of doctor-patient conversations into clinical notes by medical scribes is an essential process for effective clinical care. Pre-trained transformer models have shown a great amount of success in this area, but the domain shift from standard NLP tasks to the medical domain continues to present challenges. We build upon several recent works to show that additional pre-training with in-domain medical conversations leads to performance gains for clinical summarization. In addition to conventional evaluation metrics, we also explore a clinical named entity recognition model for concept-based evaluation. Finally, we contrast long-sequence transformers with a common transformer model, BART. Overall, our findings corroborate research in non-medical domains and suggest that in-domain pre-training combined with transformers for long sequences are effective strategies for summarizing clinical encounters.
%U https://aclanthology.org/2022.nlg4health-1.2
%P 9-22

Markdown (Informal)

[In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations](https://aclanthology.org/2022.nlg4health-1.2) (Grambow et al., NLG4Health 2022)

• In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations (Grambow et al., NLG4Health 2022)

ACL

• Colin Grambow, Longxiang Zhang, and Thomas Schaaf. 2022. In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations. In Proceedings of the First Workshop on Natural Language Generation in Healthcare, pages 9–22, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.