MultiBanAbs: A Comprehensive Multi-Domain Bangla Abstractive Text Summarization Dataset

TL;DR

This paper introduces MultiBanAbs, a large multi-domain Bangla summarization dataset with over 54,000 articles, enabling more adaptable and practical summarization models for diverse real-world texts.

Contribution

It presents a comprehensive, multi-source Bangla summarization dataset and establishes baseline results using various deep learning models, advancing NLP resources for low-resource languages.

Findings

The dataset covers multiple domains and styles, enhancing model adaptability.

Baseline models demonstrate the dataset's potential for future research.

Results show promising performance of deep learning models on the dataset.

Abstract

This study developed a new Bangla abstractive summarization dataset to generate concise summaries of Bangla articles from diverse sources. Most existing studies in this field have concentrated on news articles, where journalists usually follow a fixed writing style. While such approaches are effective in limited contexts, they often fail to adapt to the varied nature of real-world Bangla texts. In today's digital era, a massive amount of Bangla content is continuously produced across blogs, newspapers, and social media. This creates a pressing need for summarization systems that can reduce information overload and help readers understand content more quickly. To address this challenge, we developed a dataset of over 54,000 Bangla articles and summaries collected from multiple sources, including blogs such as Cinegolpo and newspapers such as Samakal and The Business Standard. Unlike single-domain resources, our dataset spans multiple domains and writing styles. It offers greater adaptability and practical relevance. To establish strong baselines, we trained and evaluated this dataset using several deep learning and transfer learning models, including LSTM, BanglaT5-small, and MTS-small. The results highlight its potential as a benchmark for future research in Bangla natural language processing. This dataset provides a solid foundation for building robust summarization systems and helps expand NLP resources for low-resource languages.