Predicting Text Relevance from Sequential Reading Behavior

In this paper we show that it is possible to make good predictions of text relevance, from only features of conscious eye movements during reading. We pay special attention to the order in which the lines of text are read, and compute simple features of this sequence. Artificial neural networks are applied to classify the relevance of the read lines. The use of ensemble techniques creates stable predictions and good generalization abilities. Using these methods we won the first competition of the PASCAL Inferring Relevance from Eye Movement Challenge [1].