@inproceedings{tangsali-etal-2022-abstractive,
title = "Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews",
author = "Tangsali, Rahul and
Vyawahare, Aditya Jagdish and
Mandke, Aditya Vyankatesh and
Litake, Onkar Rupesh and
Kadam, Dipali Dattatray",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.24",
pages = "199--203",
abstract = "Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and performing an abstractive summarization on the same remains a challenge. This paper presents a summary of the findings that we obtained based on the shared task of Multidocument Summarization for Literature Review (MSLR). We stood fourth in the leaderboards for evaluation on the MS{\^{}}2 and Cochrane datasets. We finetuned pre-trained models such as BART-large, DistilBART and T5-base on both these datasets. These models{'} accuracy was later tested with a part of the same dataset using ROUGE scores as the evaluation metrics.",
}
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<abstract>Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and performing an abstractive summarization on the same remains a challenge. This paper presents a summary of the findings that we obtained based on the shared task of Multidocument Summarization for Literature Review (MSLR). We stood fourth in the leaderboards for evaluation on the MS\² and Cochrane datasets. We finetuned pre-trained models such as BART-large, DistilBART and T5-base on both these datasets. These models’ accuracy was later tested with a part of the same dataset using ROUGE scores as the evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews
%A Tangsali, Rahul
%A Vyawahare, Aditya Jagdish
%A Mandke, Aditya Vyankatesh
%A Litake, Onkar Rupesh
%A Kadam, Dipali Dattatray
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F tangsali-etal-2022-abstractive
%X Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and performing an abstractive summarization on the same remains a challenge. This paper presents a summary of the findings that we obtained based on the shared task of Multidocument Summarization for Literature Review (MSLR). We stood fourth in the leaderboards for evaluation on the MS\² and Cochrane datasets. We finetuned pre-trained models such as BART-large, DistilBART and T5-base on both these datasets. These models’ accuracy was later tested with a part of the same dataset using ROUGE scores as the evaluation metrics.
%U https://aclanthology.org/2022.sdp-1.24
%P 199-203
Markdown (Informal)
[Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews](https://aclanthology.org/2022.sdp-1.24) (Tangsali et al., sdp 2022)
ACL