  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.
