AI RESEARCH
FACTS: Table Summarization via Offline Template Generation with Agentic Workflows
arXiv CS.CL
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ArXi:2510.13920v2 Announce Type: replace Query-focused table summarization requires generating natural language summaries of tabular data conditioned on a user query, enabling users to access insights beyond fact retrieval. Existing approaches face key limitations: table-to-text models require costly fine-tuning and struggle with complex reasoning, prompt-based LLM methods suffer from token-limit and efficiency issues while exposing sensitive data, and prior agentic pipelines often rely on decomposition, planning, or manual templates that lack robustness and scalability.