梳理文档智能数据生成方法，构建统一技术框架。
Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence
- 按数据与标签可用性划分四类生成范式。
- 提出多层级评估体系，涵盖内在质量与实际效果。
- 适合关注数据生成与文档智能的研学者。
文档智能（DI）的发展需要大规模高质量训练数据，但人工标注仍是主要瓶颈。尽管数据生成方法快速演进，现有综述多局限于单一模态或特定任务，缺乏与真实工作流对齐的统一视角。为此，本文首次建立文档智能数据生成的全面技术图谱。将数据生成重新定义为监督信号生成，并基于“数据与标签可用性”提出新分类体系，将方法归纳为四类资源中心范式：数据增强、从零生成、自动化标注和自监督信号构建。此外，建立多层级评估框架，整合内在质量与外在效用，汇总各类DI基准上的性能提升。在此统一结构下，系统剖析方法格局，揭示保真度差距等关键挑战，以及共进化生态等前沿方向。最终，通过系统化这一碎片化领域，数据生成被定位为下一代文档智能的核心引擎。
原文摘要 · Abstract (English)
The advancement of Document Intelligence (DI) demands large-scale, high-quality training data, yet manual annotation remains a critical bottleneck. While data generation methods are evolving rapidly, existing surveys are constrained by fragmented focuses on single modalities or specific tasks, lacking a unified perspective aligned with real-world workflows. To fill this gap, this survey establishes the first comprehensive technical map for data generation in DI. Data generation is redefined as supervisory signal production, and a novel taxonomy is introduced based on the "availability of data and labels." This framework organizes methodologies into four resource-centric paradigms: Data Augmentation, Data Generation from Scratch, Automated Data Annotation, and Self-Supervised Signal Construction. Furthermore, a multi-level evaluation framework is established to integrate intrinsic quality and extrinsic utility, compiling performance gains across diverse DI benchmarks. Guided by this unified structure, the methodological landscape is dissected to reveal critical challenges such as fidelity gaps and frontiers including co-evolutionary ecosystems. Ultimately, by systematizing this fragmented field, data generation is positioned as the central engine for next-generation DI.
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