An automated approach for the recognition of intended messages in grouped bar charts

Abstract

Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents.

Beyond Alternative Text and tables: Comparative Analysis of Visualization Tools and Accessibility Methods

“…Previous research has investigated the automatic generation of alternative text descriptions for various types of charts. For example, SIGHT systems generate summaries of line charts [MSMC14], bar charts [DOS * 10], and stacked bar charts [BCES19] by extracting salient features of the charts through linguistic analysis and statistical methods. In contrast, the iGraph‐Lite system uses templates to generate line chart summaries [FVL * 07, FLST13], assuming that a structured representation of the chart and underlying data is available.…”

An Approach for Chart Description Generation in Cyber–Physical–Social System

“…The second is the interpretation and application of extracted chart information. The knowledge in chart is not only the data and text information that can be directly observed but also the specific trends and other relevant characteristics of the chart itself, which is the high-level information [12,13] that can convey more important and internal knowledge.…”