# Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models

arXiv: https://arxiv.org/abs/2307.06979 · HTML: https://mlsanity.com/paper/2307.06979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 79 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Six Attributes of Unhealthy Conversation](https://mlsanity.com/paper/2010.07410/) (2020)
- [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://mlsanity.com/paper/2210.05109/) (2022)
- [Detection of Bangla Fake News using MNB and SVM Classifier](https://mlsanity.com/paper/2005.14627/) (2020)
- [Fake News Detection as Natural Language Inference](https://mlsanity.com/paper/1907.07347/) (2019)
- [Automatic Fake News Detection: Are Models Learning to Reason?](https://mlsanity.com/paper/2105.07698/) (2021)
- [Cross-lingual COVID-19 Fake News Detection](https://mlsanity.com/paper/2110.06495/) (2021)
- [Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset](https://mlsanity.com/paper/2204.02718/) (2022)
- [Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model](https://mlsanity.com/paper/1911.11062/) (2019)
- [Fake News Detection in Spanish Using Deep Learning Techniques](https://mlsanity.com/paper/2110.06461/) (2021)

## Built on by (most cited first, 2 of 2)

- [Exploration of Summarization by Generative Language Models for Automated Scoring of Long Essays](https://mlsanity.com/paper/2510.22830/) (2025)
- [BanglaMamba: Exploring State Space Models for Bangla Fake News Detection](https://mlsanity.com/paper/2608.25190/) (2026)

## Abstract (arXiv)

With the rise of social media and online news sources, fake news has become a significant issue globally. However, the detection of fake news in low resource languages like Bengali has received limited attention in research. In this paper, we propose a methodology consisting of four distinct approaches to classify fake news articles in Bengali using summarization and augmentation techniques with five pre-trained language models. Our approach includes translating English news articles and using augmentation techniques to curb the deficit of fake news articles. Our research also focused on summarizing the news to tackle the token length limitation of BERT based models. Through extensive experimentation and rigorous evaluation, we show the effectiveness of summarization and augmentation in the case of Bengali fake news detection. We evaluated our models using three separate test datasets. The BanglaBERT Base model, when combined with augmentation techniques, achieved an impressive accuracy of 96% on the first test dataset. On the second test dataset, the BanglaBERT model, trained with summarized augmented news articles achieved 97% accuracy. Lastly, the mBERT Base model achieved an accuracy of 86% on the third test dataset which was reserved for generalization performance evaluation. The datasets and implementations are available at https://github.com/arman-sakif/Bengali-Fake-News-Detection