PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
This work addresses the problem of understanding hierarchical semantic relations in lengthy, text-rich documents for researchers and AI systems, though it is incremental as it builds on existing VRD-QA frameworks.
The authors tackled the challenge of multimodal information retrieval in text-dominant documents like research journal articles by introducing PDF-MVQA, a dataset for visual question answering that focuses on retrieving entire paragraphs or visual entities across multiple pages, enhancing vision-and-language models for this domain.
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles. 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. To address this gap, we propose PDF-MVQA, which is tailored for research journal articles, encompassing multiple pages and multimodal information retrieval. Unlike traditional machine reading comprehension (MRC) tasks, our approach aims to retrieve entire paragraphs containing answers or visually rich document entities like tables and figures. Our contributions include the introduction of 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 designed to grasp textual contents and relations among document layouts simultaneously, extending page-level understanding to the entire multi-page document. Through this work, we aim to enhance the capabilities of existing vision-and-language models in handling challenges posed by text-dominant documents in VRD-QA.