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.