We present the first large-scale, cross-domain evaluation of document chunking strategies for dense retrieval, addressing a critical but underexplored aspect of retrieval-augmented systems. In our study, 36 segmentation methods spanning fixed-size, semantic, structure-aware, hierarchical, adaptive, and LLM-assisted approaches are benchmarked across six diverse knowledge domains using five different embedding models. Retrieval performance is assessed using graded relevance scores from a state-of-the-art LLM evaluator, with Normalised DCG@5 as the primary metric (complemented by Hit@5 and MRR). Our experiments show that content-aware chunking significantly improves retrieval effectiveness over naive fixed-length splitting. The top-performing strategy, Paragraph Group Chunking, achieved the highest overall accuracy (mean nDCG@5~0.459) and substantially better top-rank hit rates (Precision@1~24%, Hit@5~59%). In contrast, simple fixed-size character chunking as baselines performed poorly (nDCG@5 < 0.244, Precision@1~2-3%). We observe pronounced domain-specific differences: dynamic token sizing is strongest in biology, physics and health, while paragraph grouping is strongest in legal and maths. Larger embedding models yield higher absolute scores but remain sensitive to suboptimal segmentation, indicating that better chunking and large embeddings provide complementary benefits. In addition to accuracy gains, we quantify the efficiency trade-offs of advanced chunking. Producing more, smaller chunks can increase index size and latency. Consequently, we identify methods (like dynamic chunking) that approach an optimal balance of effectiveness and efficiency. These findings establish chunking as a vital lever for improving retrieval performance and reliability.
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen)（计算与智能研究院，哈尔滨工业大学（深圳））；Shenzhen Loop Area Institute (SLAI)（深圳环形区研究院（SLAI））
M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models
Human-inspired AI Research, Korea University（韩国大学人智AI研究所）；Computer Science and Engineering, Konkuk University（konkuk大学计算机科学与工程系）；Department of Computer Science and Engineering, Korea University（韩国大学计算机科学与工程系）
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Cloud Computing（阿里云）；Hong Kong University of Science and Technology（香港科技大学）
Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents
Department of Big Data, Chungbuk National University, Cheongju-si, South Korea（大数据系， Chungbuk国立大学，韩国Cheongju市）；Department of Computer Science, Chungbuk National University, Cheongju-si, South Korea（计算机科学系， Chungbuk国立大学，韩国Cheongju市）；BigDataLabs Co., Ltd. Department of Management Information Systems, Chungbuk National University, South Korea（BigDataLabs公司 管理信息系， Chungbuk国立大学，韩国）
HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
Human-inspired AI Research, Korea University（韩国大学人机智能研究部）；Computer Science and Engineering, Konkuk University（韩国康科大学计算机科学与工程系）；Department of Computer Science and Engineering, Korea University（韩国大学计算机科学与工程系）
Capturing Global Informativeness in Open Domain Keyphrase Extraction
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Microsoft Research（微软研究院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）；Department of Computer Science and Technology, Northeastern University（东北大学计算机科学与技术系）
KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease
Tianjin University of Technology（天津理工大学）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Peking University First Hospital（北京大学第一医院）；Peking University People’s Hospital（北京大学人民医院）；Institute of Internet Industry, Tsinghua University（清华大学互联网产业研究院）；Chinese Academy of Medical Sciences（中国医学科学院）；State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University（北京大学血管稳态与重构国家重点实验室）
