Natural Language Generation - Applications

Applications

NLG systems effectively generate jokes (see computational humor), but from a commercial perspective, the most successful NLG applications have been data-to-text systems which generate textual summaries of databases and data sets; these systems usually perform data analysis as well as text generation. In particular, several systems have been built that produce textual weather forecasts from weather data. The earliest such system to be deployed was FoG, which was used by Environment Canada to generate weather forecasts in French and English in the early 1990s. The success of FoG triggered other work, both research and commercial. Recent research in this area include an experiment which showed that users sometimes preferred computer-generated weather forecasts to human-written ones, in part because the computer forecasts used more consistent terminology, and a demonstration that statistical techniques could be used to generate high-quality weather forecasts. Recent applications include the ARNS system used to summarise conditions in US ports.

In the 1990s there was interest in using NLG to summarise financial and business data. For example the SPOTLIGHT system developed at A.C. Nielsen automatically generated readable English text based on the analysis of large amounts of retail sales data. More recently there is interest in using NLG to summarise electronic medical records. Commercial applications in this area are appearing, and researchers have shown that NLG summaries of medical data can be effective decision-support aids for medical professionals. There is also growing interest in using NLG to enhance accessibility, for example by describing graphs and data sets to blind people.

An example of an interactive use of NLG is the WYSIWYM framework. It stands for What you see is what you meant and allows users to see and manipulate the continuously rendered view (NLG output) of an underlying formal language document (NLG input), thereby editing the formal language without learning it.