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In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.
Development of clinical concept extraction applications: A methodology review
Sunyang Fu, David Chen, Sijia Liu, Sungrim Moon, Kevin J. Peterson, Feichen Shen, Yanshan Wang, Liwei Wang, Andrew Wen, Yiqing Zhao, Sunghwan Sohn, and Hongfang Liu · 1910
Earlier work this paper cites.
Automatic dialogue summary generation for customer service
Chunyi Liu, Peng Wang, Jiang Xu, Zang Li, and Jieping Ye · 1965
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Neural networks for pattern recognition
Christopher M Bishop et al · 1995
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Context: An algorithm for determining negation, experiencer, and temporal status from clinical reports
Henk Harkema, John N. Dowling, Tyler Thornblade, and Wendy W. Chapman · 2009
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li · 2016
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang · 2016
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Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction
Tait D. Shanafelt, Lotte N.Dyrbye, Christine Sinsky, Omar Hasan, Daniel Satele, Jeff Sloan, and Colin P. West · 2016
Cited alongside, same era.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter Liu, and Christopher Manning · 2017
Cited alongside, same era.
Extractive summarization of EHR discharge notes
Emily Alsentzer and Anne Kim · 2018
Cited alongside, same era.
Abstractive dialogue summarization with sentence-gated modeling optimized by dialogue acts
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Dr. summarize: Global summarization of medical dialogue by exploiting local structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain, and Anitha Kannan · 2020
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COVID-19 transforms health care through telemedicine: evidence from the field
Devin M Mann, Ji Chen, Rumi Chunara, Paul A Testa, and Oded Nov · 2020
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Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training, 2020
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou · 2020
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Chih-Wen Goo and Yun-Nung Chen · 2018
Cited alongside, same era.
Learning to summarize radiology findings
Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, and Curtis P. Langlotz · 2018
Cited alongside, same era.
Evaluating the factual consistency of abstractive text summarization, 2019
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu · 2019
Cited alongside, same era.
Generating soap notes from doctor-patient conversations, 2020a
Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, and Zachary C. Lipton
Cited in the paper.
Extracting structured data from physician-patient conversations by predicting noteworthy utterances
Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey P Bigham, and Zachary C Lipton
Cited in the paper.
Topic-aware pointer-generator networks for summarizing spoken conversations, 2019b
Zhengyuan Liu, Angela Ng, Sheldon Lee, Ai Ti Aw, and Nancy F. Chen
Cited in the paper.
Stephen Roller, Naman Goyal Emily Dinan, Mary Williamson Da Ju, Jing Xu Yinhan Liu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, and Jason Weston · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Later among the works it cites.
What makes good in-context examples for gpt- 3 3 ?, 2021
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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