With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days. But still the number of datasets is low in number. In this work, we (Team HakunaMatata) study how these multilingual models perform on the datasets which have Indian languages as source and target text while performing summarization. We experimented with IndicBART and mT5 models to perform the experiments and report the ROUGE-1, ROUGE-2, ROUGE-3 and ROUGE-4 scores as a performance metric.

翻译：随着mBART、mT5、IndicBART等多语言模型的出现，低资源印度语言的摘要任务近年备受关注。然而，相关数据集的数量仍然有限。在本研究中，我们（HakunaMatata团队）探讨了这些多语言模型在以印度语言为源语言和目标语言的摘要数据集上的表现。我们通过IndicBART和mT5模型进行实验，并以ROUGE-1、ROUGE-2、ROUGE-3和ROUGE-4分数作为性能指标进行报告。