Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarization

• URL: http://arxiv.org/abs/2507.22829v1
• Date: Wed, 30 Jul 2025 16:42:19 GMT
• Title: Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarization
• Authors: Weijia Zhang, Songgaojun Deng, Evangelos Kanoulas,
• Abstract summary: 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.<n> Experiments on three public benchmarks show that SPaGe consistently outperforms prior models in both single- and multi-table settings.
• Score: 21.1381898110636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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