Abstract
Natural language and visualization are two complementary modalities of human communication that play a crucial role in conveying information effectively. While visualizations help people discover trends, patterns, and anomalies in data, natural language descriptions help explain these insights. Thus, combining text with visualizations is a prevalent technique for effectively delivering the core message of the data. Given the rise of natural language generation (NLG), there is a growing interest in automatically creating natural language descriptions for visualizations, which can be used as chart captions, answering questions about charts, or telling data-driven stories. In this survey, we systematically review the state of the art on NLG for visualizations and introduce a taxonomy of the problem. The NLG tasks fall within the domain of Natural Language Interfaces (NLI) for visualization, an area that has garnered significant attention from both the research community and industry. To narrow down the scope of the survey, we primarily concentrate on the research works that focus on text generation for visualizations. To characterize the NLG problem and the design space of proposed solutions, we pose five Wh-questions, why and how NLG tasks are performed for visualizations, what the task inputs and outputs are, as well as where and when the generated texts are integrated with visualizations. We categorize the solutions used in the surveyed papers based on these "five Wh-questions." Finally, we discuss the key challenges and potential avenues for future research in this domain.
Abstract (translated)
自然语言和可视化是人类交流的两种互补维度，在传达信息方面起着关键作用。虽然可视化帮助人们发现数据中的趋势、模式和异常，自然语言描述则有助于解释这些洞见。因此，将文本与可视化相结合是一种普遍的技巧，可以有效地传递数据的核心信息。随着自然语言生成（NLG）的兴起，人们对自动为可视化创建自然语言描述产生了浓厚兴趣，这些描述可以用于图表标题、回答关于图表的问题或讲述数据驱动的故事。在本次调查中，我们系统地回顾了NLG在可视化领域的前沿研究，并引入了一个分类来解决这个问题。NLG任务属于自然语言交互（NLI）可视化域，该领域从研究社区和产业界都受到了很大的关注。为了缩小调查范围，我们主要关注关注研究重点在文本生成上的可视化作品。通过提出五个“为什么以及如何”的问题，我们试图阐明NLG任务如何为可视化执行，任务输入和输出是什么，以及生成的文本如何与可视化结合。我们根据这些“五个为什么”对调查论文中使用的解决方案进行了分类。最后，我们讨论了该领域未来研究的关键挑战和潜在方向。
URL
https://arxiv.org/abs/2409.19747
