Using OLAP and Data Mining for Content Planning in Natural Language Generation

We present a new approach to content determination and content organization in the context of natural language generation for quantitative database summaries. Three key properties make our work innovative and interesting: (1) we developed a new text planning approach to deals with the content organization of a data set into a summary report, for example a Data Mining discovery; (2) the approach is domain independent; (3) it covers a significant class of database summary applications. 1 Research Context: Executive Summary Generation In this paper, we present a new approach for content determination and organization in quantitative data summarization. This approach has been developed for HYSSOP (HYpertext Summary System of On-line analytical Processing) which generates hypertext reports for OLAP summaries and Data Mining (DM) discoveries. HYSSOP is itself part of the Intelligent Decision-Support System called MATRIKS (Multidimensional Analysis and Textual Reporting for Insight Knowledge Search). MATRIKS aims to provide a comprehensive knowledge discovery environment through seamless integration of data warehousing, OLAP, DM, expert system and natural language generation technologies. The architecture of MATRIKS is given in Fig. 1. It extends previous cutting-edge environments for Knowledge Discovery in Databases (KDD) such as DBMiner [4] by the integration of: • a data warehouse hypercube exploration expert system allowing automation and expertise legacy of dimensional data warehouse exploration strategies developed by human data analyst using OLAP queries and data mining tools;