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This paper presents an approach to affective video summarisation based on the facial expressions (FX) of viewers. A facial expression recognition system was deployed to capture a viewer's face and his/her expressions. The user's facial expressions were analysed to infer personalised affective scenes from videos. We proposed two models, pronounced level and expression's change rate, to generate affective summaries using the FX data. Our result suggested that FX can be a promising source to exploit for affective video summaries that can be tailored to individual preferences.