Chart summarization is a crucial task for blind and visually impaired individuals as it is their primary means of accessing and interpreting graphical data. Crafting high-quality descriptions is challenging because it requires precise communication of essential details within the chart without vision perception. Many chart analysis methods, however, produce brief, unstructured responses that may contain significant hallucinations, affecting their reliability for blind people. To address these challenges, this work presents three key contributions: (1) We introduce the AltChart dataset, comprising 10,000 real chart images, each paired with a comprehensive summary that features long-context, and semantically rich annotations. (2) We propose a new method for pretraining Vision-Language Models (VLMs) to learn fine-grained chart representations through training with multiple pretext tasks, yielding a performance gain with ${\sim}2.5\%$. (3) We conduct extensive evaluations of four leading chart summarization models, analyzing how accessible their descriptions are. Our dataset and codes are publicly available on our project page: https://github.com/moured/AltChart.

翻译：图表摘要对于盲人和视障人士至关重要，因为这是他们获取和解读图形数据的主要方式。生成高质量描述颇具挑战性，因为这需要在不依赖视觉感知的情况下精确传达图表中的关键细节。然而，许多图表分析方法会生成简短、结构松散的回应，其中可能包含严重幻觉，从而影响其对盲人的可靠性。为解决这些问题，本研究提出三项关键贡献：（1）我们引入AltChart数据集，包含10,000张真实图表图像，每张图像配有包含长上下文和语义丰富标注的综合摘要；（2）我们提出一种新的视觉语言模型预训练方法，通过多预文任务训练学习细粒度图表表示，性能提升约2.5%；（3）我们对四种主流图表摘要模型进行广泛评估，分析其描述的可访问性。我们的数据集和代码已在项目页面公开：https://github.com/moured/AltChart。