An immediate goal of text data mining is to construct synopses of the hypertextual material: summaries of the topics which are covered by the texts in data base according to criteria defined by the scriptor.
Another goal is to identify salient points: concise lists of different topics, if possible in order of importance, adjustable in depth.
An important goal is also taxonomy (keywords in the data base): determination of the topics in the documents which are (or should be) of interest to the reader. This is to be followed by classification: the grouping of documents containing different topics, either as defined by the scriptor, or as defined by the information content. The most valuable help for the hypertextual reader consists of the identification of dependencies of the different topics on each other, especially of unexpected relationships.