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Videos are a commonly-used type of content in learning during Web search.Many e-learning platforms provide quality content, but sometimes educationalvideos are long and cover many topics. Humans are good in extracting importantsections from videos, but it remains a significant challenge for computers. Inthis paper, we address the problem of assigning importance scores to videosegments, that is how much information they contain with respect to the overalltopic of an educational video. We present an annotation tool and a new datasetof annotated educational videos collected from popular online learningplatforms. Moreover, we propose a multimodal neural architecture that utilizesstate-of-the-art audio, visual and textual features. Our experimentsinvestigate the impact of visual and temporal information, as well as thecombination of multimodal features on importance prediction.

Classification of Important Segments in Educational Videos using Multimodal Features