通过布局感知对齐和结构感知推理实现科学文档的异构元素感知跨版本差异分析

中文摘要 AI 辅助

科学文档的跨版本差异分析在学术出版和技术文档中至关重要，但具有挑战性，因为科学文档包含文本、表格、公式、图形等异构元素。现有基于文本序列的方法常丢失布局和结构信息，基于图像的方法缺乏语义可解释性且对渲染变化敏感。本文提出一个布局感知的异构元素感知框架，将文档版本分解为语义类型元素，通过联合建模空间、内容和结构兼容性的对齐优先机制建立跨版本对应，并对对齐的元素对进行类型感知差异推理。它支持跨文本、表格、公式和图形的统一变化检测、定位、结构感知分析以及对齐/匹配评估。实验表明该框架优于特定元素的基线方法，消融和敏感性分析证实了跨版本对齐、特定类型表示、结构感知推理和兼容性权重设计的有效性。这些结果表明异构元素感知差异分析为现实编辑生产场景中的科学文档比较提供了一个强大且可解释的解决方案。

英文摘要

Cross-version differencing of scientific documents is essential in scholarly publishing and technical documentation, but remains challenging because scientific documents are page-structured artifacts containing heterogeneous elements such as text, tables, formulas, figures, and layout cues. Existing text-sequence-based methods often lose layout and structural information, while image-based methods lack semantic interpretability and are sensitive to rendering variation. To address these limitations, this paper proposes a layout-aware heterogeneous element-aware framework for scientific document differencing. The framework decomposes document versions into semantically typed elements, establishes cross-version correspondence through an alignment-first mechanism that jointly models spatial, content, and structural compatibility, and performs type-aware difference reasoning over aligned element pairs. It supports unified change detection, localization, structure-awareness analysis, and alignment/matching evaluation across text, tables, formulas, and figures. Experiments on real-world scientific PDF data from journal production proofreading workflows show that the proposed framework consistently outperforms element-specific baselines. It achieves detection F1 scores of 0.903, 0.855, 0.862, and 0.845 for text, tables, formulas, and figures, respectively, with further improvements in localization, structure awareness, and matching quality. Ablation and sensitivity analyses confirm the effectiveness of cross-version alignment, type-specific representations, structure-aware reasoning, and compatibility-weight design. These results demonstrate that heterogeneous element-aware differencing provides a robust and interpretable solution for scientific document comparison in realistic editorial production scenarios.

发表机构

• Beijing Renhe Information Technology Co., Ltd.（北京人和信息技术有限公司）
• Key Laboratory of Digital Publishing and Total Process Management of Scientific and Technical Journals（科技期刊数字出版与全程管理重点实验室）

机构由 AI 辅助整理，请以论文原文为准。