用智能代理实现企业表格多维数据精准摘要，提升业务洞察力。
Multi-Dimensional Summarization Agents with Context-Aware Reasoning over Enterprise Tables
- 构建多代理流水线，分步处理切片、变化检测与上下文构建。
- 达83%数据忠实度，4.4/5相关性评分，显著捕捉微妙业务权衡。
- 适合需要深度分析企业报表的决策者与数据分析师。
我们提出一种基于大语言模型（LLM）智能体的新型框架，用于跨多维度总结结构化企业数据。传统表格转文本模型往往缺乏对层级结构和上下文感知差异的推理能力，而这在商业报告中至关重要。我们的方法引入多智能体流水线，通过切片代理、波动检测代理、上下文构建代理和基于LLM的生成代理，实现数据提取、分析与摘要。实验结果表明，该框架优于传统方法，在数据忠实度上达到83%，显著提升重大变化覆盖度，并在关键决策洞察相关性上获得4.4/5评分。尤其在涉及细微权衡的场景中表现突出，例如价格上升带来的收入增长但单位销量下降的情况，现有方法常忽略或表述不精确。我们在Kaggle数据集上评估，证实其在忠实度、相关性和洞察质量方面均显著优于基线方法。
原文摘要 · Abstract (English)
We propose a novel framework for summarizing structured enterprise data across multiple dimensions using large language model (LLM)-based agents. Traditional table-to-text models often lack the capacity to reason across hierarchical structures and context-aware deltas, which are essential in business reporting tasks. Our method introduces a multi-agent pipeline that extracts, analyzes, and summarizes multi-dimensional data using agents for slicing, variance detection, context construction, and LLM-based generation. Our results show that the proposed framework outperforms traditional approaches, achieving 83\% faithfulness to underlying data, superior coverage of significant changes, and high relevance scores (4.4/5) for decision-critical insights. The improvements are especially pronounced in categories involving subtle trade-offs, such as increased revenue due to price changes amid declining unit volumes, which competing methods either overlook or address with limited specificity. We evaluate the framework on Kaggle datasets and demonstrate significant improvements in faithfulness, relevance, and insight quality over baseline table summarization approaches.
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