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Charts are commonly used for exploring data and communicating insights.Generating natural language summaries from charts can be very helpful forpeople in inferring key insights that would otherwise require a lot ofcognitive and perceptual efforts. We present Chart-to-text, a large-scalebenchmark with two datasets and a total of 44,096 charts covering a wide rangeof topics and chart types. We explain the dataset construction process andanalyze the datasets. We also introduce a number of state-of-the-art neuralmodels as baselines that utilize image captioning and data-to-text generationtechniques to tackle two problem variations: one assumes the underlying datatable of the chart is available while the other needs to extract data fromchart images. Our analysis with automatic and human evaluation shows that whileour best models usually generate fluent summaries and yield reasonable BLEUscores, they also suffer from hallucinations and factual errors as well asdifficulties in correctly explaining complex patterns and trends in charts.

Chart-to-Text: A Large-Scale Benchmark for Chart Summarization