Bangla Key2Text: Text Generation from Keywords for a Low Resource Language

TL;DR

This paper presents Bangla Key2Text, a large dataset for keyword-driven text generation in Bangla, along with baseline models and evaluations, to advance low-resource language NLP research.

Contribution

It introduces a new large-scale dataset of Bangla keyword-text pairs and provides baseline models and benchmarks for keyword-to-text generation in Bangla.

Findings

Fine-tuning models significantly improves generation quality.

Task-specific models outperform zero-shot large language models.

The dataset and models are publicly released for future research.

Abstract

This paper introduces \textit{Bangla Key2Text}, a large-scale dataset of $2.6$ million Bangla keyword--text pairs designed for keyword-driven text generation in a low-resource language. The dataset is constructed using a BERT-based keyword extraction pipeline applied to millions of Bangla news texts, transforming raw articles into structured keyword--text pairs suitable for supervised learning. To establish baseline performance on this new benchmark, we fine-tune two sequence-to-sequence models, \texttt{mT5} and \texttt{BanglaT5}, and evaluate them using multiple automatic metrics and human judgments. Experimental results show that task-specific fine-tuning substantially improves keyword-conditioned text generation in Bangla compared to zero-shot large language models. The dataset, trained models, and code are publicly released to support future research in Bangla natural language generation and keyword-to-text generation tasks.