Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901

Earlier work this paper cites.

Automated methods for the summarization of electronic health records

Rimma Pivovarov and Noémie Elhadad. 2015 · 2015

Earlier work this paper cites.

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019

Earlier work this paper cites.

A novel system for extractive clinical note summarization using EHR data

Jennifer Liang, Ching-Huei Tsou, and Ananya Poddar. 2019 · 2019

Earlier work this paper cites.

Ontology-aware clinical abstractive summarization

Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, and Ross W Filice. 2019 · 2019

Earlier work this paper cites.

Studying summarization evaluation metrics in the appropriate scoring range

Maxime Peyrard. 2019 · 2019

Earlier work this paper cites.

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Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz, Gagandeep Singh, Fabian Stemmer, Weiyi Sun, Paul Vozila, Thomas Lin, and Ranjani Ramamurthy. 2020 · 2020

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Cited alongside, same era.

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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020

Cited alongside, same era.

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Medically aware GPT-3 as a data generator for medical dialogue summarization

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Leveraging pretrained models for automatic summarization of doctor-patient conversations

Original

Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, and Matthew R Gormley. 2021 · 2021

Later among the works it cites.

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Original

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Later among the works it cites.

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Later among the works it cites.

News summarization and evaluation in the era of gpt-3

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Later among the works it cites.

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