Low Resource Summarization using Pre-trained Language Models

TL;DR

This paper introduces a method for low-resource language summarization using adapted transformer models, demonstrating effective performance with a new Urdu dataset and a smaller model size.

Contribution

The paper presents a novel adaptation of transformer models for low-resource summarization and constructs a new Urdu dataset for benchmarking.

Findings

urT5 achieves up to 46.35 ROUGE-1 score

Model size reduced by 44.78% compared to mT5

Competitive results comparable to high-resource models

Abstract

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.