用结构化计划提升表格摘要的准确性和可扩展性
Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarization
- 采用结构化计划TaSoF替代模糊的自然语言步骤
- 在多表任务中准确率提升，支持并行执行
- 适合需要可靠生成和复杂依赖处理的场景
查询聚焦的表格摘要需复杂推理，传统方法依赖模糊的自然语言（NL）计划，难以转化为可执行程序如SQL，限制了可扩展性，尤其在多表任务中。为此，我们提出范式转变：使用结构化表示。引入受多智能体系统启发的新结构化计划TaSoF，以及框架SPaGe，包含三个阶段：1）结构化规划，从查询生成TaSoF；2）基于图的执行，通过有向循环图将计划步骤转为SQL并建模依赖关系，实现并行执行；3）摘要生成，输出聚焦查询的总结。该方法显式捕捉复杂依赖，提升可靠性。在三个公开基准上的实验表明，SPaGe在单表和多表设置下均持续优于先前模型，验证了结构化表示在鲁棒性和可扩展性上的优势。
原文摘要 · Abstract (English)
Query-focused table summarization requires complex reasoning, often approached through step-by-step natural language (NL) plans. However, NL plans are inherently ambiguous and lack structure, limiting their conversion into executable programs like SQL and hindering scalability, especially for multi-table tasks. To address this, we propose a paradigm shift to structured representations. We introduce a new structured plan, TaSoF, inspired by formalism in traditional multi-agent systems, and a framework, SPaGe, that formalizes the reasoning process in three phases: 1) Structured Planning to generate TaSoF from a query, 2) Graph-based Execution to convert plan steps into SQL and model dependencies via a directed cyclic graph for parallel execution, and 3) Summary Generation to produce query-focused summaries. Our method explicitly captures complex dependencies and improves reliability. Experiments on three public benchmarks show that SPaGe consistently outperforms prior models in both single- and multi-table settings, demonstrating the advantages of structured representations for robust and scalable summarization.
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