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With the growing popularity of short-form video sharing platforms such as\em{Instagram} and \em{Vine}, there has been an increasing need for techniquesthat automatically extract highlights from video. Whereas prior works haveapproached this problem with heuristic rules or supervised learning, we presentan unsupervised learning approach that takes advantage of the abundance ofuser-edited videos on social media websites such as YouTube. Based on the ideathat the most significant sub-events within a video class are commonly presentamong edited videos while less interesting ones appear less frequently, weidentify the significant sub-events via a robust recurrent auto-encoder trainedon a collection of user-edited videos queried for each particular class ofinterest. The auto-encoder is trained using a proposed shrinking exponentialloss function that makes it robust to noise in the web-crawled training data,and is configured with bidirectional long short term memory(LSTM)~\cite{LSTM:97} cells to better model the temporal structure of highlightsegments. Different from supervised techniques, our method can infer highlightsusing only a set of downloaded edited videos, without also needing theirpre-edited counterparts which are rarely available online. Extensiveexperiments indicate the promise of our proposed solution in this challengingunsupervised settin

Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders