标题： 多维摘要代理人在企业表格上的上下文感知推理 显示英文标题

标题： Multi-Dimensional Summarization Agents with Context-Aware Reasoning over Enterprise Tables

摘要： 我们提出了一种新颖的框架，用于使用基于大型语言模型（LLM）的代理对多维结构化企业数据进行摘要。 传统表格到文本模型通常缺乏在层次结构和上下文感知差异之间进行推理的能力，这在业务报告任务中是必不可少的。 我们的方法引入了一个多代理流程，通过切片、方差检测、上下文构建和基于LLM的生成代理来提取、分析和总结多维数据。 我们的结果表明，所提出的框架优于传统方法，在底层数据的忠实度达到83%，显著变化的覆盖范围更优，并且对于决策关键见解的相关性评分（4.4/5）很高。 在涉及细微权衡的类别中，改进尤为明显，例如由于价格变动导致的收入增加而单位销量下降，竞争对手的方法要么忽略，要么以有限的特异性来处理。 我们在Kaggle数据集上评估了该框架，并展示了在忠实度、相关性和见解质量方面相对于基线表格摘要方法的显著改进。 显示英文摘要

摘要： 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.