Workshop on Document Intelligence Understanding
It tackles the problem of inefficient document processing for researchers and industry developers, but is incremental as it builds on existing datasets and tasks.
This workshop addressed the need for automated document understanding across domains like business and law by organizing a data challenge on the PDFVQA dataset, which evaluates models on full-document, multi-page question-answering to advance beyond single-page analysis.
Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also released a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the structural and contextual understandings of proposed models on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.