Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures. (2020.findings-emnlp)
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| Challenge: | Summarization of medical conversations addresses a very real need in medical practice: capturing the most important aspects of a medical encounter. |
| Approach: | They propose a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient’s medical history. |
| Outcome: | The proposed model captures most or all of the information in 80% of the medical conversations making it a realistic alternative to costly manual summarization by medical experts. |
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
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MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022.findings-emnlp)
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| Challenge: | Existing models for summarizing medical conversations do not take clinical knowledge into account and are difficult to control. |
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| Challenge: | Conversations are the natural communication format for people. |
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| Challenge: | Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters. |
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| Challenge: | a system that extracts pertinent medical information from dialogues between clinicians and patients is proposed . entering data into EMRs is currently slow and error-prone, and clinicians spend up to 50% of their time on data entry. |
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| Challenge: | Existing datasets for summarization of medical conversations are limited to conversation-summary pairs . a novel annotation framework is proposed to capture the summarizing process via an annotation task . |
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| Challenge: | Creating digital SOAP notes is burdensome and contributes to physician burnout . authors propose a pipeline to generate these notes based on transcripts of clinical conversations . |
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Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations (2021.findings-emnlp)
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Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, Matthew R. Gormley
| Challenge: | Using pretrained transformer models for automatically summarizing doctor-patient conversations presents challenges . limited training data, domain shift, long and noisy transcripts, and high target summary variability are challenges compared to human annotators. |
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Abstractive Meeting Summarization: A Survey (2023.tacl-1)
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
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