MMVQA: A Comprehensive Dataset for Investigating Multipage Multimodal Information Retrieval in PDF-based Visual Question Answering

Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly with lengthy textual content. Existing studies primarily focus on real-world documents with sparse text, while challenges persist in comprehending the hierarchical semantic relations among multiple pages to locate multimodal components. The paper introduces MMVQA, a dataset tailored for research journal articles, encompassing multiple pages and multimodal retrieval. Our approach aims to retrieve entire paragraphs containing answers or visually rich document entities like tables and figures. The main contribution is introducing a comprehensive PDF Document VQA dataset, allowing the examination of semantically hierarchical layout structures in text-dominant documents. We also present new VRD-QA frameworks to grasp textual contents and relations among document layouts simultaneously, extending page-level understanding to the entire multi-page document. We aim to enhance the capabilities of existing vision-and-language models in handling challenges posed by text-dominant documents in VRD-QA. Code and Appendix are in https://github.com/adlnlp/pdfmvqa.