The research on text summarization for low-resource Indian languages has been
limited due to the availability of relevant datasets. This paper presents a
summary of various deep-learning approaches used for the ILSUM 2022 Indic
language summarization datasets. The ISUM 2022 dataset consists of news
articles written in Indian English, Hindi, and Gujarati respectively, and their
ground-truth summarizations. In our work, we explore different pre-trained
seq2seq models and fine-tune those with the ILSUM 2022 datasets. In our case,
the fine-tuned SoTA PEGASUS model worked the best for English, the fine-tuned
IndicBART model with augmented data for Hindi, and again fine-tuned PEGASUS
model along with a translation mapping-based approach for Gujarati. Our scores
on the obtained inferences were evaluated using ROUGE-1, ROUGE-2, and ROUGE-4
as the evaluation metrics.

本文介绍了使用不同的预训练 seq2seq 模型在 ILSUM 2022 数据集上微调的结果，其中使用 PEGASUS 模型得到了最佳的英文结果，使用具有增强数据的 IndicBART 模型得到了最佳的印地语结果，并使用转换映射为基础的方法对瓜拉提语进行了微调并重新运行了 PEGASUS 模型，使用 ROUGE-1、ROUGE-2 和 ROUGE-4 作为评估度量方法进行了推理成果的评估。