Generating summaries of line graphs

Abstract

This demo presents a Natural Language Generation (NLG) system that generates summaries of informational graphics, specifically simple line graphs, present in popular media. The system is intended to capture the high-level knowledge conveyed by the graphic and its outstanding visual features. It comprises a content selection phase that extracts the most important content of the graphic, an organization phase, which orders the propositions in a coherent manner, and a realization phase that uses the text surrounding the article to make decisions on the choice of lexical items and amount of aggregation applied to the propositions to generate the summary of the graphic.