Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews

Rahul Tangsali, Aditya Jagdish Vyawahare, Aditya Vyankatesh Mandke, Onkar Rupesh Litake, Dipali Dattatray Kadam


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 MSˆ2 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.
Anthology ID:
2022.sdp-1.24
Volume:
Proceedings of the Third Workshop on Scholarly Document Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
199–203
Language:
URL:
https://aclanthology.org/2022.sdp-1.24
DOI:
Bibkey:
Cite (ACL):
Rahul Tangsali, Aditya Jagdish Vyawahare, Aditya Vyankatesh Mandke, Onkar Rupesh Litake, and Dipali Dattatray Kadam. 2022. Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 199–203, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews (Tangsali et al., sdp 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.sdp-1.24.pdf
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