“Document Information Extraction (DIE) is a crucial task for extracting key information fromvisually-rich documents. The typical pipeline approach for this task involves Optical Charac-ter Recognition (OCR), serializer, Semantic Entity Recognition (SER), and Relation Extraction(RE) modules. However, this pipeline presents significant challenges in real-world scenariosdue to issues such as unnatural text order and error propagation between different modules. Toaddress these challenges, we propose a novel tagging-based method – Global TaggeR (GTR),which converts the original sequence labeling task into a token relation classification task. Thisapproach globally links discontinuous semantic entities in complex layouts, and jointly extractsentities and relations from documents. In addition, we design a joint training loss and a jointdecoding strategy for SER and RE tasks based on GTR. Our experiments on multiple datasetsdemonstrate that GTR not only mitigates the issue of text in the wrong order but also improvesRE performance. Introduction”