Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EmphasisChecker, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.

翻译：近期研究表明，当图表和标题同时强调数据的相同方面时，读者倾向于将这种双重强调的特征作为核心结论记忆；若两者重点不一致，读者则会依赖图表形成结论，可能错失标题中的信息。通过对真实世界来源（如新闻媒体、民调报告、政府报告、学术论文及Tableau Public）中280组图表-标题对的调查，我们发现实践中标题往往未能强调与图表相同的信息，这可能限制读者有效获取作者预期传达的信息。受调查结果启发，我们提出EmphasisChecker这一交互式工具，可高亮视觉突出的图表特征、标题文本强调的特征，并显示两者间的重点差异。该工具实现了基于Ramer-Douglas-Peucker算法的时间序列显著特征检测器，以及能提取标题中时间引用与数据描述并将其与图表数据匹配的文本引用提取器。这些信息使作者能够对比两种模态强调的特征，快速识别差异并进行必要修订。用户研究证实，该工具在创作图表与标题时兼具实用性与易用性。