RAG-based QA has emerged as a powerful method for processing long industrial documents. However, conventional text chunking approaches often neglect complex and long industrial document structures, causing information loss and reduced answer quality. To address this, we introduce MultiDocFusion, a multimodal chunking pipeline that integrates: (i) detection of document regions using vision-based document parsing, (ii) text extraction from these regions via OCR, (iii) reconstruction of document structure into a hierarchical tree using large language model (LLM)-based document section hierarchical parsing (DSHP-LLM), and (iv) construction of hierarchical chunks through DFS-based grouping. Extensive experiments across industrial benchmarks demonstrate that MultiDocFusion improves retrieval precision by 8–15% and ANLS QA scores by 2–3% compared to baselines, emphasizing the critical role of explicitly leveraging document hierarchy for multimodal document-based QA. These significant performance gains underscore the necessity of structure-aware chunking in enhancing the fidelity of RAG-based QA systems.

Rather than continuing the conversation based on personalized or implicit information, the existing conversation system generates dialogue by focusing only on the superficial content. To solve this problem, FoCus was recently released. FoCus is a persona-knowledge grounded dialogue generation dataset that leverages Wikipedia’s knowledge and personal persona, focusing on the landmarks provided by Google, enabling user-centered conversation. However, a closer empirical study is needed since research in the field is still in its early stages. Therefore, we fling two research questions about FoCus. “Is the FoCus whether for conversation or question answering?” to identify the structural problems of the dataset. “Does the FoCus model do real knowledge blending?” to closely demonstrate that the model acquires actual knowledge. As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.