Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning

2026-05-08

AI Summary

This work addresses the challenge of cross-version comparison of scientific documents, which contain heterogeneous elements—such as text, tables, equations, and figures—alongside complex layouts, rendering existing methods inadequate in simultaneously preserving structural fidelity and ensuring semantic interpretability. The authors propose a heterogeneous-element-aware framework that decomposes documents by semantic type and jointly models spatial layout, content semantics, and structural compatibility to achieve layout-aware alignment and structure-aware, type-specific difference reasoning. This approach is the first to unify multimodal element change detection, localization, structural analysis, and matching evaluation across document versions. Evaluated on real-world scientific PDFs, it achieves F1 scores of 0.903, 0.855, 0.862, and 0.845 for text, tables, equations, and figures, respectively, significantly outperforming current baselines.

Abstract

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.

Problem

• scientific document differencing
• heterogeneous elements
• cross-version comparison
• layout awareness
• structure awareness

Innovation

• heterogeneous element-aware
• layout-aware alignment
• structure-aware reasoning
• cross-version differencing
• scientific document comparison