让文档布局的视觉关系可解释，用自然语言讲清空间模式。
Context-Aware Explanations for Spatialized Document Layouts
- 基于语义与空间上下文生成解释，识别聚类、异常等模式。
- 用户研究显示，带空间信息的解释更助于理解布局组织。
- 适合做文档分析的学者或数据探索者使用。
空间化文档布局广泛用于文本语料的探索性分析，但理解文档的空间组织及其区域间关系仍具挑战。现有方法多聚焦内容摘要或布局生成机制，难以支持对布局内部空间关系的理解。我们提出CAPE框架，一种基于上下文的解释方法，生成依托于文档语义和布局衍生空间上下文的自然语言解释。CAPE识别显著的空间模式（如聚类、子组、异常值、连接文档），构建多层次上下文表示以指导基于大模型的解释生成。支持AI引导概览与用户驱动探索，提供多层级解释粒度。我们在新闻与学术文档布局上验证CAPE，并在受控用户研究中对比关键词基线和仅内容基线。结果表明，空间化解释比仅内容基线更被感知为有帮助，有助于理解文档布局的空间组织。
原文摘要 · Abstract (English)
Spatialized document layouts are widely used for exploratory analysis of text corpora, but interpreting the spatial organization of documents and the relationships between regions remains challenging. Existing approaches primarily summarize document content or explain how layouts are generated, providing limited support for understanding spatial relationships within the layout itself. We present CAPE, a context-aware explanation framework that generates natural-language explanations grounded in both document semantics and layout-derived spatial context. CAPE identifies salient spatial patterns (e.g., clusters, subgroups, outliers, and bridging documents) and constructs multi-level contextual representations to guide LLM-based explanation generation. It supports both AI-guided overview and user-driven exploration, with explanations available at multiple levels of detail. We demonstrate CAPE on news and scholarly document layouts and evaluate it in a controlled user study against keyword-based and content-only LLM baselines. Our results suggest that spatially grounded explanations are perceived as more helpful than content-only baselines for interpreting the spatial organization of document layouts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。