Structured Information Extraction

Structured information extraction (SIE) aims to automatically convert unstructured text, such as scientific papers or medical reports, into structured, machine-readable formats. Current research heavily utilizes large language models (LLMs), often augmented with retrieval mechanisms, to achieve this, with a focus on improving accuracy and robustness across diverse document types and languages, including handling issues like document skew and low-resource settings. These advancements are crucial for various applications, including accelerating scientific discovery through knowledge graph construction, improving healthcare efficiency via automated data analysis, and streamlining business processes through automated data entry. The field is also actively developing unified benchmarks and frameworks to facilitate more robust and comparable evaluations of different SIE approaches.