Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework
It addresses the need for comprehensive synthesis and evaluation in text-to-structure generation, which is incremental as it reviews existing work rather than proposing new methods.
This systematic review tackles the problem of transforming unstructured text into structured formats for agentic AI by synthesizing methodologies, datasets, and metrics, and introduces a universal evaluation framework to establish text-to-structure as foundational infrastructure.
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.