Using a natural language generation approach to document simulation results

Simulation experiments generate large data sets that must be converted into recommendations for decision makers. This article explores using a Natural Language Generation (NLG) approach for writing summaries of simulation experiments. The article discusses the steps required to convert simulation experiment data to text and highlights the unique aspects of data to text for simulation experiments. Automation of report generation can potentially reduce the time and cost of simulation studies and improve the reporting of results. A prototype software system was developed and applied to a simulation to illustrate the benefits of a NLG approach.