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        <title>A Comprehensive Text Optimization Approach to Bangla Summarization</title>
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            <title>Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)</title>
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    <abstract>The task of Bengali text optimization demands not only the generation of concise and coherent summaries but also grammatical accuracy, semantic appropriateness, and factual reliability. This study presents a dual-phase optimization framework for Bengali text summarization that integrates entity-preserving preprocessing and abstractive generation with mT5, followed by refinement through sentence ranking, entity consistency enforcement, and optimization with instruction-tuned LLMs such as mBART. Evaluations using ROUGE, BLEU,BERTScore, and human ratings of fluency, adequacy, coherence, and readability show consistent gains over baseline summarizers. By embedding grammatical and factual safe guards into the summarization pipeline, this study establishes a robust and scalable benchmark for Bengali NLP, advancing text optimization research. Our model achieves 0.54 ROUGE-1 and 0.88 BERTScore on BANSData, outperforming recent multilingual baselines.</abstract>
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%0 Conference Proceedings
%T A Comprehensive Text Optimization Approach to Bangla Summarization
%A Haider, Irtifa
%A Alam, Shanjida
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F haider-alam-2025-comprehensive
%X The task of Bengali text optimization demands not only the generation of concise and coherent summaries but also grammatical accuracy, semantic appropriateness, and factual reliability. This study presents a dual-phase optimization framework for Bengali text summarization that integrates entity-preserving preprocessing and abstractive generation with mT5, followed by refinement through sentence ranking, entity consistency enforcement, and optimization with instruction-tuned LLMs such as mBART. Evaluations using ROUGE, BLEU,BERTScore, and human ratings of fluency, adequacy, coherence, and readability show consistent gains over baseline summarizers. By embedding grammatical and factual safe guards into the summarization pipeline, this study establishes a robust and scalable benchmark for Bengali NLP, advancing text optimization research. Our model achieves 0.54 ROUGE-1 and 0.88 BERTScore on BANSData, outperforming recent multilingual baselines.
%U https://aclanthology.org/2025.banglalp-1.13/
%P 151-160

Markdown (Informal)

[A Comprehensive Text Optimization Approach to Bangla Summarization](https://aclanthology.org/2025.banglalp-1.13/) (Haider & Alam, BanglaLP 2025)

• A Comprehensive Text Optimization Approach to Bangla Summarization (Haider & Alam, BanglaLP 2025)

ACL

• Irtifa Haider and Shanjida Alam. 2025. A Comprehensive Text Optimization Approach to Bangla Summarization. In Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025), pages 151–160, Mumbai, India. Association for Computational Linguistics.