Bangla News Headline Generation

Abstract

This project presents a comparative study on Bangla news headline generation using two transformer-based models: the multilingual mT5 and the monolingual BT5-base. Aimed at addressing the scarcity of effective headline generation tools for low- resource languages like Bangla, the study evaluates both models on a curated

dataset using standard performance metrics. While both models demonstrated stable training behavior, BT5-base exhibited faster convergence and lower validation loss, indicating more efficient learning. Evaluation results reveal a stark contrast in

output quality: BT5-base achieved a ROUGE-1 F1 score of over 56% and a ROUGE- 2 score of 45.92%, significantly outperforming mT5, whose scores remained below 3%

across all ROUGE metrics. Furthermore, BT5-base attained a 21.33% exact match rate and showed markedly lower Character Error Rate (CER) and Word Error Rate (WER), highlighting its superior ability to produce semantically and lexically aligned headlines. These results affirm the effectiveness of domain-specific pretraining, as the Bangla-focused BT5-base consistently delivered more fluent, accurate, and culturally appropriate headlines than the multilingual mT5 model. The findings underscore the value of monolingual transformer models for text generation in underrepresented languages and contribute a practical foundation for future advancements in Bangla NLP applications.