The number of news reports published online is now so great that it is impossible 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 potentially valuable goal. Traditional methods of information management such as information filtering, information retrieval and collaborative filtering are unsuited to the task of filtering news. This abstract presents a framework that permits the identification of interesting news by means of the identification 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.
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