AI 中文总结
该研究针对多文档摘要验证难题，提出SVS视觉分析系统，含两种二维布局，实验表明其可提升验证准确性、降低工作量，SUMMARY-GUIDED布局综合表现最优。
AI 中文摘要
大型语言模型越来越多地从文档集合生成摘要以支持意义建构和报告，但验证摘要陈述是否基于源材料仍然困难。在多文档摘要（MDS）中，证据分布在许多源文档中，可能不完整、冲突或缺失。我们提出摘要验证空间（SVS），这是一种用于通过空间文档组织和协调来源可视化来验证多文档摘要的视觉分析系统。为支持可扩展的验证，我们研究两种替代二维画布布局：SUMMARY-GUIDED布局，该布局根据与摘要句子的对齐方式组织文档；以及SOURCE-GUIDED布局，该布局根据语义相似性排列文档。协调的来源可视化随后明确了摘要内容、源文档和支持证据之间的关系，使用户能够在验证过程中追踪支持、矛盾和缺失的证据。一个任务驱动的使用场景说明了一种审核工作流程，用户使用这些布局定位相关文档和覆盖缺口，然后检查链接的主张和源证据以做出自己的基于依据的判断。在来源保持不变的比较研究中，两种空间布局相对于线性基线都提高了总体准确性并降低了工作量，在相关性任务上的提升最为明显。SUMMARY-GUIDED布局在准确性、效率、置信度和工作量之间提供了最强的整体平衡。
英文摘要
Large language models increasingly generate summaries from collections of documents to support sensemaking and reporting, but verifying whether summary statements are grounded in source materials remains difficult. In multi-document summarization (MDS), evidence is distributed across many source documents and may be incomplete, conflicting, or missing. We present Summary Verification Space (SVS), a visual analytics system for verifying multi-document summaries through spatial document organization and coordinated provenance visualization. To support scalable verification, we investigate two alternative 2D canvas layouts: a SUMMARY-GUIDED layout that organizes documents by alignment with summary sentences, and a SOURCE-GUIDED layout that arranges documents by semantic similarity. Coordinated provenance visualization then makes relationships among summary content, source documents, and supporting evidence explicit, enabling users to trace support, contradiction, and missing evidence during verification. A task-driven usage scenario illustrates an auditing workflow in which users use the layouts to locate relevant documents and coverage gaps, then inspect linked claims and source evidence to make their own grounding judgments. In a comparative study with provenance held constant, both spatial layouts improved aggregate accuracy and reduced workload relative to a linear baseline, with the clearest gains on relevance tasks. The SUMMARY-GUIDED layout provides the strongest overall balance of accuracy, efficiency, confidence, and workload.
CommentsAccepted to IEEE VIS 2026; 13 pages, including appendices (11-page paper plus 2-page appendices)
