Name
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. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahul Tangsali | 1 | 0 | 0.34 |
| Aditya Jagdish Vyawahare | 2 | 0 | 0.34 |
| Aditya Vyankatesh Mandke | 3 | 0 | 0.34 |
| Onkar Rupesh Litake | 4 | 0 | 0.34 |
| Dipali Dattatray Kadam | 5 | 0 | 0.34 |