将无结构文本转为表格等结构化数据，助力智能体系统发展
Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework
- 梳理文本转结构的技术路径与挑战
- 提出统一评估框架，覆盖多种输出格式
- 适合研究智能体、知识提取的学者参考
AI系统向自主代理和上下文感知检索演进，亟需将无结构文本转化为表格、知识图谱、图表等结构化形式。此类转换支撑摘要生成、数据挖掘等关键应用，但现有研究缺乏对方法、数据集与评估指标的系统性整合。本文综述文本转结构技术及其面临挑战，评估现有数据集与评价标准，并展望未来研究方向。同时提出通用评估框架，确立文本转结构作为下一代AI系统的基础基础设施。
原文摘要 · Abstract (English)
The evolution of AI systems toward agentic operation and context-aware retrieval necessitates transforming unstructured text into structured formats like tables, knowledge graphs, and charts. While such conversions enable critical applications from summarization to data mining, current research lacks a comprehensive synthesis of methodologies, datasets, and metrics. This systematic review examines text-to-structure techniques and the encountered challenges, evaluates current datasets and assessment criteria, and outlines potential directions for future research. We also introduce a universal evaluation framework for structured outputs, establishing text-to-structure as foundational infrastructure for next-generation AI systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。