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.

本研究采用 IndicBART 和 mT5 模型，研究它们在印度语言数据集上的摘要性能，结果以 ROUGE-1、ROUGE-2、ROUGE-3 和 ROUGE-4 分数为性能指标。

使用多语言变压器模型对印度语言进行摘要

Summarizing Indian Languages using Multilingual Transformers based Models

In this paper, we study pre-trained sequence-to-sequence models for a group
of related languages, with a focus on Indic languages. We present IndicBART, a
multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic
languages and English. IndicBART utilizes the orthographic similarity between
Indic scripts to improve transfer learning between similar Indic languages. We
evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and
extreme summarization. Our experiments on NMT and extreme summarization show
that a model specific to related languages like IndicBART is competitive with
large pre-trained models like mBART50 despite being significantly smaller. It
also performs well on very low-resource translation scenarios where languages
are not included in pre-training or fine-tuning. Script sharing, multilingual
training, and better utilization of limited model capacity contribute to the
good performance of the compact IndicBART model.

本文探讨了预训练的序列到序列模型在相关语言群中的应用，关注印欧语言。我们介绍了 IndicBART - 一种针对 11 种印欧语言和英语的多语言序列到序列预训练模型。通过利用印欧语言之间的拼写相似性，IndicBART 在类似印欧语言之间进行转移学习，获得了不错的性能表现。研究表明，即使相对较小的 IndicBART 模型在机器翻译和极端摘要等任务上也具有与大型预训练模型相媲美的性能，甚至在很低资源的情况下依然表现优异。模型参数共享，多语言训练等技术有助于提高 IndicBART 模型的性能水平。