Automatic chart to text summarization is an effective tool for the visually
impaired people along with providing precise insights of tabular data in
natural language to the user. A large and well-structured dataset is always a
key part for data driven models. In this paper, we propose ChartSumm: a
large-scale benchmark dataset consisting of a total of 84,363 charts along with
their metadata and descriptions covering a wide range of topics and chart types
to generate short and long summaries. Extensive experiments with strong
baseline models show that even though these models generate fluent and
informative summaries by achieving decent scores in various automatic
evaluation metrics, they often face issues like suffering from hallucination,
missing out important data points, in addition to incorrect explanation of
complex trends in the charts. We also investigated the potential of expanding
ChartSumm to other languages using automated translation tools. These make our
dataset a challenging benchmark for future research.

本文介绍了 ChartSumm 数据集，它是一个大规模的基准数据集，由 84,363 个图表及其元数据和描述组成，涵盖了各种主题和图表类型。该数据集可用于生成短和长的摘要，用以提供给视障人士以及为其他数据驱动模型的训练提供基础。通过使用 ChartSumm 数据集进行大量实验，发现仍有调查的空间，如资料遗漏，产生幻觉，以及对图表中复杂趋势的不恰当解释。作者研究了扩展 ChartSumm 数据集以适用于其他语言的潜力，这使我们的数据集成为未来研究的有挑战性的基准。