MDPIJournal Paper2025

A Review of State-of-the-Art Transformers for Bengali Text Summarization

Abstract

This comprehensive review examines both Extractive and Abstractive approaches to Bengali Text Summarization (BTS). While deep learning has significantly advanced summarization for resource-rich languages like English, Bengali remains largely underexplored. The review covers BTS research from 2007 to 2023, analyzing trends, datasets, preprocessing methods, methodologies, evaluations, and challenges. It draws from 106 journal and conference papers to offer insights into emerging topics in Bengali Abstractive summarization. The study also augments the review with experiments using transformer models from Hugging Face and publicly available datasets to assess ROUGE score accuracy.

Key Achievements

Comprehensive review covering 2007–2023 (106 papers analyzed)
XL-SUM dataset with MT5 model identified as best performer
Covers both Extractive and Abstractive approaches
Experimental validation using Hugging Face transformers
Identifies key challenges: lack of standard datasets, pre-training models
Provides recommendations for future Bengali NLP research

Topics

Bengali NLPText SummarizationTransformersLSTMMT5Review