  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 remains a challenge.
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 cooccurrence 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.
