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Video summaries come in many forms, from traditional single-image thumbnails,animated thumbnails, storyboards, to trailer-like video summaries. Contentcreators use the summaries to display the most attractive portion of theirvideos; the users use them to quickly evaluate if a video is worth watching.All forms of summaries are essential to video viewers, content creators, andadvertisers. Often video content management systems have to generate multipleversions of summaries that vary in duration and presentational forms. Wepresent a framework ReconstSum that utilizes LSTM-based autoencoderarchitecture to extract and select a sparse subset of video frames or keyshotsthat optimally represent the input video in an unsupervised manner. The encoderselects a subset from the input video while the decoder seeks to reconstructthe video from the selection. The goal is to minimize the difference betweenthe original input video and the reconstructed video. Our method is easilyextendable to generate a variety of applications including static videothumbnails, animated thumbnails, storyboards and "trailer-like" highlights. Wespecifically study and evaluate two most popular use cases: thumbnailgeneration and storyboard generation. We demonstrate that our methods generatebetter results than the state-of-the-art techniques in both use cases.

From Thumbnails to Summaries - A single Deep Neural Network to Rule Them All