Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG)\u00a0capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically\u00a0extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current\u00a0systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying\u00a0visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the\u00a0data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used\u00a0to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this\u00a0paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and\u00a0communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both\u00a0alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging\u00a0data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder\u2019s design and features,\u00a0we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant\u00a0feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions\u00a0surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG)\u00a0capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically\u00a0extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current\u00a0systems, users are typically required to read the system-generated 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