Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework

2025-08-17

AI Summary

Text-to-structured generation (e.g., tables, knowledge graphs, charts) for agent-centric AI is a foundational infrastructure enabling context-aware retrieval and autonomous reasoning, yet suffers from fragmented methodologies, scarce standardized datasets, and inconsistent evaluation protocols. Method: We conduct a systematic literature review integrating techniques from NLP, information extraction, knowledge representation, and machine learning to establish the first holistic analytical framework—comprising task taxonomy, benchmark dataset inventory, and unified evaluation metrics. Contribution/Results: We introduce the first general-purpose evaluation framework for structured output generation, explicitly identifying methodological limitations and core challenges (e.g., fidelity, composability, and reasoning-aware assessment). We comprehensively map research gaps and affirm the centrality of this direction in next-generation AI systems, providing both theoretical grounding and practical guidance for future algorithmic development and empirical validation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Structured Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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.

Problem

• Transforming unstructured text into structured formats
• Lack of comprehensive synthesis in current research
• Establishing evaluation framework for structured outputs

Innovation

• Systematic review of text-to-structure generation techniques
• Universal evaluation framework for structured outputs
• Transforming unstructured text into structured formats