Year: 2026
Type: conference-paper
Abstract: Retrieval-augmented generation (RAG) for document-based open-domain question answering (ODQA) over large industrial corpora faces two core bottlenecks: routing to the correct document and combining scattered evidence.Flat text chunks and page-level images often fail to (i) identify the right document among thousands of candidates and (ii) connect multimodal evidence, such as tables and figures, within a fixed token budget.We propose HiKEY, a hierarchical tree-based multimodal retrieval framework that treats document hierarchy as a first-class retrieval signal.Rather than simply chunking text, HiKEY uses Document Hierarchical Parsing (DHP) to reconstruct a logical heterogeneous graph with explicit parent-child relations.At query time, HiKEY follows a hierarchical coarse-to-fine process: it first performs global routing with hierarchical indexes to prune the corpus, and then ranks sections with a multimodal fusion strategy that selects the most discriminative evidence.It finally builds a token-efficient evidence subgraph through hybrid structural-semantic packing.Experiments on ODQA benchmarks show that HiKEY outperforms page-and chunk-based baselines, improving retrieval recall by up to 12.9 points and end-to-end QA by up to 6.8 points.
Institutions: Korea University, Korea University, Konkuk University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G6181273566, G7102533171
OpenAlex ID: W7166815578