The ILSUM shared task focuses on text summarization for two major Indian
languages- Hindi and Gujarati, along with English. In this task, we experiment
with various pretrained sequence-to-sequence models to find out the best model
for each of the languages. We present a detailed overview of the models and our
approaches in this paper. We secure the first rank across all three sub-tasks
(English, Hindi and Gujarati). This paper also extensively analyzes the impact
of k-fold cross-validation while experimenting with limited data size, and we
also perform various experiments with a combination of the original and a
filtered version of the data to determine the efficacy of the pretrained
models.

本研究主要关注以预训练序列到序列模型为基础进行文本摘要的任务，研究内容涉及英语、印地语和古吉拉特语。我们尝试了多种模型，对不同模型在三个子任务上的效果进行了对比，同时对数据大小和过滤对模型效果的影响进行了分析。研究发现在有限的数据大小下，k 折交叉验证可以显著提高模型效果。