  Video summaries come in many forms, from traditional single-image thumbnails,
animated thumbnails, storyboards, to trailer-like video summaries. Content
creators use the summaries to display the most attractive portion of their
videos; the users use them to quickly evaluate if a video is worth watching.
All forms of summaries are essential to video viewers, content creators, and
advertisers. Often video content management systems have to generate multiple
versions of summaries that vary in duration and presentational forms. We
present a framework ReconstSum that utilizes LSTM-based autoencoder
architecture to extract and select a sparse subset of video frames or keyshots
that optimally represent the input video in an unsupervised manner. The encoder
selects a subset from the input video while the decoder seeks to reconstruct
the video from the selection. The goal is to minimize the difference between
the original input video and the reconstructed video. Our method is easily
extendable to generate a variety of applications including static video
thumbnails, animated thumbnails, storyboards and "trailer-like" highlights. We
specifically study and evaluate two most popular use cases: thumbnail
generation and storyboard generation. We demonstrate that our methods generate
better results than the state-of-the-art techniques in both use cases.
