Chart captions that explain complex trends and patterns are important for improving a reader’s ability to comprehend and retain the data being presented. And for people with visual disabilities, the information in a caption often provides their only means of understanding the chart.
But writing effective, detailed captions is a labor-intensive process. While autocaptioning techniques can alleviate this burden, they often struggle to describe cognitive features that provide additional context.
To help people author high-quality chart captions, MIT researchers have developed a dataset to improve automatic captioning systems. Using this tool, researchers could teach a machine-learning model to vary the level of complexity and type of content included in a chart caption based on the needs of users.
CHUCKYSCARNAGE 