Hierarchical Context-Aware Summarization for Complex Korean Administrative Tables via Multi-Stage Prompt Engineering

Interpreting and summarizing complex structured tabular data, particularly in specialized domains such as Korean administration, presents significant challenges due to intricate structures and domain-specific terminology. While Large Language Models (LLMs) offer promising capabilities, their direct application often results in information loss and misinterpretation. Existing solutions frequently necessitate extensive and resource-intensive model fine-tuning. To address these limitations, we propose Hierarchical Context-Aware Summarization (HCAS), a novel framework utilizing sophisticated prompt engineering and multi-stage reasoning. HCAS generates high-quality, human-friendly explanatory summaries for highlighted regions within complex Korean administrative tables, critically, without requiring large-scale model fine-tuning. It deconstructs the task into three distinct stages: Contextual Key Information Extraction, Explanatory Narrative Skeleton Construction, and Fluency and Readability Optimization, progressively enriching contextual understanding and refining output quality. Our comprehensive experiments on the NIKL Korean Table Explanation Benchmark demonstrate that HCAS consistently achieves superior performance, surpassing traditional fine-tuning methods and advanced in-context learning baselines on leading Korean LLMs. Further analyses validate HCAS's ability to produce factually accurate, coherent, and professionally appropriate summaries, while offering significant advantages in efficiency and resource utilization.