@inproceedings{michalopoulos-etal-2022-medicalsum,
title = "{M}edical{S}um: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations",
author = "Michalopoulos, George and
Williams, Kyle and
Singh, Gagandeep and
Lin, Thomas",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.349",
doi = "10.18653/v1/2022.findings-emnlp.349",
pages = "4741--4749",
abstract = "We introduce MedicalSum, a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). The novel knowledge augmentation is performed in three ways: (i) introducing a guidance signal that consists of the medical words in the input sequence, (ii) leveraging semantic type knowledge in UMLS to create clinically meaningful input embeddings, and (iii) making use of a novel weighted loss function that provides a stronger incentive for the model to correctly predict words with a medical meaning. By applying these three strategies, MedicalSum takes clinical knowledge into consideration during the summarization process and achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2{\%} ROUGE-1 error reduction in the PE section) when producing medical summaries of patient-doctor conversations.",
}
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<abstract>We introduce MedicalSum, a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). The novel knowledge augmentation is performed in three ways: (i) introducing a guidance signal that consists of the medical words in the input sequence, (ii) leveraging semantic type knowledge in UMLS to create clinically meaningful input embeddings, and (iii) making use of a novel weighted loss function that provides a stronger incentive for the model to correctly predict words with a medical meaning. By applying these three strategies, MedicalSum takes clinical knowledge into consideration during the summarization process and achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% ROUGE-1 error reduction in the PE section) when producing medical summaries of patient-doctor conversations.</abstract>
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%0 Conference Proceedings
%T MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations
%A Michalopoulos, George
%A Williams, Kyle
%A Singh, Gagandeep
%A Lin, Thomas
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F michalopoulos-etal-2022-medicalsum
%X We introduce MedicalSum, a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). The novel knowledge augmentation is performed in three ways: (i) introducing a guidance signal that consists of the medical words in the input sequence, (ii) leveraging semantic type knowledge in UMLS to create clinically meaningful input embeddings, and (iii) making use of a novel weighted loss function that provides a stronger incentive for the model to correctly predict words with a medical meaning. By applying these three strategies, MedicalSum takes clinical knowledge into consideration during the summarization process and achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% ROUGE-1 error reduction in the PE section) when producing medical summaries of patient-doctor conversations.
%R 10.18653/v1/2022.findings-emnlp.349
%U https://aclanthology.org/2022.findings-emnlp.349
%U https://doi.org/10.18653/v1/2022.findings-emnlp.349
%P 4741-4749
Markdown (Informal)
[MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations](https://aclanthology.org/2022.findings-emnlp.349) (Michalopoulos et al., Findings 2022)
ACL