Computing and Informatics

The number of news reports published online is too large for any person to read all of them. Not all of these reports are equally interesting. Automating the identification and evaluation of interest in news is therefore a valuable goal. This paper presents a framework that permits the identification of interesting news by means of violated expectations. Facts derived from news reports, expectations and related background knowledge can be used to (i) justify the decision to rate news as interesting, (ii) explain why the information in the report is unexpected and, (iii) explain the context in which the report appears. Explanations supported by this framework are general purpose explanations based on the data in the system. The explanations are natural language renditions of first order logic facts and rules.