%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 %Y Cohan, Arman %Y Feigenblat, Guy %Y Freitag, Dayne %Y Ghosal, Tirthankar %Y Herrmannova, Drahomira %Y Knoth, Petr %Y Lo, Kyle %Y Mayr, Philipp %Y Shmueli-Scheuer, Michal %Y de Waard, Anita %Y Wang, Lucy Lu %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