# Multimodal Summarization of User-Generated Videos
## 1 Introduction  
Recent advances in the fields of digital imaging and electronics have allowed the integration of high definition optical sensors into affordable mobile phones and action cameras. Consequently, we have witnessed an exponential growth of user-generated video content. Moreover, a continuously increasing number of users tend to capture their daily life and other activities, such as sports, trips and so forth, and share them via social networks such as Facebook ([[http://www.facebook.com](http://www.facebook.com)]([http://www.facebook.com](http://www.facebook.com))), Instagram ([[http://www.instagram.com](http://www.instagram.com)]([http://www.instagram.com](http://www.instagram.com))), Twitter ([[http://www.twitter.com](http://www.twitter.com)]([http://www.twitter.com](http://www.twitter.com))) and so forth, or video-sharing platforms such as YouTube ([[http://www.youtube.com](http://www.youtube.com)]([http://www.youtube.com](http://www.youtube.com))), Vimeo ([[http://www.vimeo.com](http://www.vimeo.com)]([http://www.vimeo.com](http://www.vimeo.com))) and so forth. According to the official YouTube statistics [1], every day its users watch over 1 billion hours of video and also upload more than 500 h of video every minute. It is generally anticipated that the aforementioned numbers will be constantly growing for the next years. As the amount of available information grows, the amount of assistance that users need to efficiently browse huge video collections [2] and to derive useful information within large videos is rising. To fulfil the aforementioned ever-growing needs, many research efforts have shifted towards the task of _video summarization_. In brief, this task aims to create a condensed version of a given video sequence [3]; when a user watches this version, she/he should immediately capture the most important parts of the video's content. Apart from efficient browsing and retrieval of videos for entertainment purposes [4], applications of video summarization include summarization of surveillance videos [5], of medical videos [6], of large videos captured by unmanned aired vehicles (UAVs) [7] and so forth. Approaches to video summarization may be classified into four major categories, based on the type of audiovisual visual cues produced and presented to their end user [3], that is, their output. More specifically, output of video summarization algorithms may consist of a) keyframes [8], which are extracted video frames, presented in order and areoften denoted as \"static\" summaries; b) (a set of) video segments [9], which are often denoted as \"dynamic\" summaries and consist of an obvious extension of keyframes and preserve both audio and motion of videos; c) graphical cues [10] which complement other cues by some type of graphical-based syntax to further enhance the interpretation of summaries by the end user; and d) automatically generated textual annotations [11], which aim to provide efficient summaries of video content. In this paper, we propose adopting a supervised video summarization technique to produce short dynamic summaries for user-generated videos. Our approach belongs to a sub-category often denoted as _video skimming_[12; 13]. Such approaches are based on uni- or multi-modal features, extracted from the video. Their output consists of parts of the original video that have been selected as significant, while preserving their temporal order. As denoted by [PERSON] and [PERSON] [13], video skimming summarization approaches allow for better understanding of the original video by end users, based solely on its summary. Therefore, such approaches have recently drawn increased attention within the research community. More specifically, in this work, we propose the use of supervised knowledge from both audio and visual domains, to achieve summarizations of user-generated videos. In particular, we analyze a given video stream by splitting it into one-second segments of audio and visual representations.