# BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion

arXiv: https://arxiv.org/abs/2501.01069 · HTML: https://mlsanity.com/paper/2501.01069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 75 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [Hooks in the Headline: Learning to Generate Headlines with Controlled Styles](https://mlsanity.com/paper/2004.01980/) (2020)
- [BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla](https://mlsanity.com/paper/2205.11081/) (2022)
- [Generating Representative Headlines for News Stories](https://mlsanity.com/paper/2001.09386/) (2020)
- [BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP](https://mlsanity.com/paper/2309.13173/) (2023)
- [Self-Attentive Model for Headline Generation](https://mlsanity.com/paper/1901.07786/) (2019)
- [Advances of Transformer-Based Models for News Headline Generation](https://mlsanity.com/paper/2007.05044/) (2020)
- [Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation](https://mlsanity.com/paper/2004.03875/) (2020)
- [V=arta: A Large-Scale Headline-Generation Dataset for Indic Languages](https://mlsanity.com/paper/2305.05858/) (2023)
- [Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes](https://mlsanity.com/paper/2210.09389/) (2022)
- [Mukhyansh: A Headline Generation Dataset for Indic Languages](https://mlsanity.com/paper/2311.17743/) (2023)
- [Contrastive Learning enhanced Author-Style Headline Generation](https://mlsanity.com/paper/2211.03305/) (2022)
- [How to Generate Popular Post Headlines on Social Media?](https://mlsanity.com/paper/2309.09949/) (2023)
- [AfriHG: News headline generation for African Languages](https://mlsanity.com/paper/2412.20223/) (2024)

## Built on by (most cited first, 1 of 1)

- [When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generation](https://mlsanity.com/paper/2608.15879/) (2026)

## Abstract (arXiv)

Automatic text summarization, particularly headline generation, remains a critical yet underexplored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category, and aspect. This limitation significantly hinders their effectiveness and overall performance. This study addresses this limitation by introducing a novel corpus, BeliN (Bengali Religious News) - comprising religious news articles from prominent Bangladeshi online newspapers, and MultiGen - a contextual multi-input feature fusion headline generation approach. Leveraging transformer-based pre-trained language models such as BanglaT5, mBART, mT5, and mT0, MultiGen integrates additional contextual features - including category, aspect, and sentiment - with the news content. This fusion enables the model to capture critical contextual information often overlooked by traditional methods. Experimental results demonstrate the superiority of MultiGen over the baseline approach that uses only news content, achieving a BLEU score of 18.61 and ROUGE-L score of 24.19, compared to baseline approach scores of 16.08 and 23.08, respectively. These findings underscore the importance of incorporating contextual features in headline generation for low-resource languages. By bridging linguistic and cultural gaps, this research advances natural language processing for Bengali and other underrepresented languages. To promote reproducibility and further exploration, the dataset and implementation code are publicly accessible at https://github.com/akabircs/BeliN.