@inproceedings{kolhatkar-etal-2023-team,
title = "Team Converge at {P}rob{S}um 2023: Abstractive Text Summarization of Patient Progress Notes",
author = "Kolhatkar, Gaurav and
Paranjape, Aditya and
Gokhale, Omkar and
Kadam, Dipali",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.50",
doi = "10.18653/v1/2023.bionlp-1.50",
pages = "510--515",
abstract = "In this paper, we elaborate on our approach for the shared task 1A issued by BioNLP Workshop 2023 titled Problem List Summarization. With an increase in the digitization of health records, a need arises for quick and precise summarization of large amounts of records. With the help of summarization, medical professionals can sieve through multiple records in a short span of time without overlooking any crucial point. We use abstractive text summarization for this task and experiment with multiple state-of-the-art models like Pegasus, BART, and T5, along with various pre-processing and data augmentation techniques to generate summaries from patients{'} progress notes. For this task, the metric used was the ROUGE-L score. From our experiments, we conclude that Pegasus is the best-performing model on the dataset, achieving a ROUGE-L F1 score of 0.2744 on the test dataset (3rd rank on the leaderboard).",
}
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<abstract>In this paper, we elaborate on our approach for the shared task 1A issued by BioNLP Workshop 2023 titled Problem List Summarization. With an increase in the digitization of health records, a need arises for quick and precise summarization of large amounts of records. With the help of summarization, medical professionals can sieve through multiple records in a short span of time without overlooking any crucial point. We use abstractive text summarization for this task and experiment with multiple state-of-the-art models like Pegasus, BART, and T5, along with various pre-processing and data augmentation techniques to generate summaries from patients’ progress notes. For this task, the metric used was the ROUGE-L score. From our experiments, we conclude that Pegasus is the best-performing model on the dataset, achieving a ROUGE-L F1 score of 0.2744 on the test dataset (3rd rank on the leaderboard).</abstract>
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%0 Conference Proceedings
%T Team Converge at ProbSum 2023: Abstractive Text Summarization of Patient Progress Notes
%A Kolhatkar, Gaurav
%A Paranjape, Aditya
%A Gokhale, Omkar
%A Kadam, Dipali
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kolhatkar-etal-2023-team
%X In this paper, we elaborate on our approach for the shared task 1A issued by BioNLP Workshop 2023 titled Problem List Summarization. With an increase in the digitization of health records, a need arises for quick and precise summarization of large amounts of records. With the help of summarization, medical professionals can sieve through multiple records in a short span of time without overlooking any crucial point. We use abstractive text summarization for this task and experiment with multiple state-of-the-art models like Pegasus, BART, and T5, along with various pre-processing and data augmentation techniques to generate summaries from patients’ progress notes. For this task, the metric used was the ROUGE-L score. From our experiments, we conclude that Pegasus is the best-performing model on the dataset, achieving a ROUGE-L F1 score of 0.2744 on the test dataset (3rd rank on the leaderboard).
%R 10.18653/v1/2023.bionlp-1.50
%U https://aclanthology.org/2023.bionlp-1.50
%U https://doi.org/10.18653/v1/2023.bionlp-1.50
%P 510-515
Markdown (Informal)
[Team Converge at ProbSum 2023: Abstractive Text Summarization of Patient Progress Notes](https://aclanthology.org/2023.bionlp-1.50) (Kolhatkar et al., BioNLP 2023)
ACL