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，当图表和标题共同强调数据的相同方面时，读者倾向于将双重强调的特征视为核心要点；若两者强调内容不一致，读者则主要依赖图表形成理解，可能忽略标题文本中的信息。通过对280组来自真实数据源（如新闻媒体、民意报告、政府文件、学术论文及Tableau Public平台）的图表-标题配对进行调研，我们发现实践中标题常未与图表强调相同信息，这可能会限制读者有效获取作者预期传达的意图。基于调研发现，我们提出EmphasisChecker——一款交互式工具，可高亮显示视觉上显著的图表特征、标题文本强调的特征，以及两者间的强调偏差。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，以及可识别标题中时间参考与数据描述并将其与图表数据匹配的文本引用提取器。通过此信息，作者可对比两种模态强调的特征，快速发现偏差并作出必要修正。用户研究证实，该工具在图表与标题创作过程中兼具实用性与易用性。