With the advent of Deep Learning based Artificial Neural Networks models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model \textit{urT5} with up to 44.78\% reduction in size as compared to \textit{mT5} can capture contextual information of low resource language effectively with evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) at par with state-of-the-art models in high resource language English \textit{(PEGASUS: 47.21, BART: 45.14 on XSUM Dataset)}. The proposed method provided a baseline approach towards extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

翻译：随着基于深度学习的人工神经网络模型的出现，自然语言处理在文本数据处理的效率和准确性方面取得了显著进步。然而，此类研究主要局限于英语等高资源语言，而低资源语言仍面临训练数据集及模型（甚至包括基线评估结果）可用资源匮乏的问题。考虑到低资源语言资源有限，我们提出了一种方法，将基于自注意力机制的Transformer架构模型（mBERT、mT5）适配于低资源摘要生成任务，并辅以在低资源语言乌尔都语中构建的新基线数据集（76,500个文章-摘要对）。选择新闻（公共可用来源）作为应用领域，有望使所提方法可适用于其他资源有限的语种复制。相较于mT5，我们适配的摘要模型urT5体积缩小达44.78%，却能有效捕捉低资源语言的上下文信息，其评估得分（最高达46.35 ROUGE-1、77 BERTScore）与高资源语言英语的最先进模型（PEGASUS：47.21、BART：45.14，基于XSUM数据集）持平。所提方法为抽取式及生成式摘要提供了基线方案，并在有限资源设定下取得了具有竞争力的评估结果。