Abstract
Cross-version differencing of scientific documents is essential in scholarly publishing and technical documentation. However, it remains challenging due to the heterogeneous elements like text, tables, formulas, figures, and layout cues present in page-structured artifacts. Existing text-sequence-based methods often lose layout and structural information, while image-based methods lack semantic interpretability and are sensitive to rendering variations. 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, establishing 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 show that the proposed framework consistently outperforms element-specific baselines with detection F1 scores of 0.903, 0.855, 0.862, and 0.845 for text, tables, formulas, and figures, respectively, alongside 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.
Blogger's Review: The introduction of this framework addresses the shortcomings of traditional methods, particularly in handling complex documents while effectively retaining layout and structural information. Its practicality and scalability show promise for widespread application in academia, offering a fresh perspective on scientific document differencing that deserves attention.