Authors - Duy Nguyen Ngoc, Hiep Nghia Phan Abstract - Large Language Models (LLMs) have recently shown strong capabilities in automatic text summarization. However, applying these models to lowresource languages such as Vietnamese remains challenging due to limited training resources and language-specific characteristics. In this work, we ex-amine whether prompt optimization can improve Vietnamese summarization quality without modifying model parameters. This paper introduces a Vietnamese-aware Prompt Optimization Framework that refines prompt instructions by combining task-specific guidance, role-based prompting, linguistic con-straints, and iterative feedback. The generated summaries are assessed using both automatic evaluation metrics and human judgments to examine the ef-fectiveness of different prompt designs. We evaluate the proposed approach on a benchmark Vietnamese multi-aspect opinion dataset using several commercial and open-source LLMs, including GPT-4, Claude 3, Gemini 1.5, and PhoGPT, and compare their performance with Vietnamese pre-trained summarization models such as ViT5 and BARTpho. Our experiments show that refining prompts consistently improves summary quality across the evaluated models. In particular, GPT-4 with the optimized prompt achieves an 8.7% increase in ROUGE-L and receives higher human evaluation scores for fluency and factual consistency than the standard prompting setting.
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