End to End Information Extraction

End-to-end information extraction (IE) aims to directly extract structured information from unstructured text or even visually rich documents, bypassing the need for intermediate steps like text segmentation or separate feature engineering. Current research emphasizes developing robust models, often based on transformer architectures, that can handle diverse data types and achieve high accuracy with limited training data, focusing on techniques to improve span identification and the integration of visual and textual information. This approach holds significant promise for automating tasks across various domains, from legal analysis and archival research to processing visually complex documents like receipts and forms, ultimately improving efficiency and enabling large-scale data analysis.