The advances in AI-enabled techniques have accelerated the creation and automation of visualizations in the past decade. However, presenting visualizations in a descriptive and generative format is still challenging. Moreover, current visualization embedding methods focus on standalone visualizations, neglecting the importance of contextual information for multi-view visualizations. To address this issue, we propose a new representation model, Chart2Vec, to learn a universal embedding of visualizations with context-aware information. Chart2Vec aims to support a wide range of downstream visualization tasks such as recommendation and storytelling. Our model considers both structural and semantic information of visualizations in declarative specifications. To enhance the context-aware capability, Chart2Vec employs multi-task learning on both supervised and unsupervised tasks concerning the co-occurrence of visualizations. We evaluate our method through an ablation study, a user study, and a quantitative comparison. The results verified the consistency of our embedding method with human cognition and showed its advantages over existing methods.

翻译：过去十年中，人工智能技术的进步加速了可视化的创建与自动化。然而，以描述性和生成式格式呈现可视化仍然充满挑战。此外，现有可视化嵌入方法侧重于独立可视化，忽略了多视图可视化中上下文信息的重要性。为解决这一问题，我们提出了一种新型表示模型Chart2Vec，用于学习具有上下文感知信息的可视化通用嵌入。Chart2Vec旨在支持推荐和故事叙述等广泛的后续可视化任务。我们的模型同时考虑了声明式规范中可视化的结构信息和语义信息。为增强上下文感知能力，Chart2Vec基于可视化的共现关系，在监督与无监督任务上采用多任务学习策略。我们通过消融研究、用户研究和定量比较评估了该方法，结果验证了所提嵌入方法与人类认知的一致性，并展示了其相较于现有方法的优势。