## 2 State of The Art
### Video Summarization  
To allow users to get the essence of a video in a shorter time, researchers have proposed many approaches which are referred to in literature as video summarization. Video summarization is defined as a technique that facilitates video content consumption by extracting the essential information of a video to produce a compact version [45]. It would not be farfetched to say that in video summarization, importance is usually attributed to visual features. For example, [PERSON] et al. propose an approach to build a video summary or video skim by Logical Story Units (LSU) [16]. They create LSUs with salient features such as motion intensity in MPEG I-frames and P-frames and face detection in frames trained over Hidden Markov Models (HMM). [PERSON] et al. create a video summary by extracting color features from frames trained by unsupervised clustering by k-means clustering algorithm [46], while [PERSON] et al. use color histograms in I-frames of MPEG encoding and a noise filtering algorithm to a generate summary of videos [47]. [PERSON] et al. try to create a multi-video summary of user-generated videos based on aesthetic-guided criterion [48]. [PERSON] et al. extend the idea of clustering key-frames by proposing a graph-based hierarchal approach [49].  
However multimodal features are also getting considerable attention due to the added value they bring. For example [PERSON] et al. use both visual and audio features to propose a hybrid approach combining content truncation and adaptive fast-forwarding to offer users a summary of video [50]. [PERSON] et al. use low-level multimodal features and a fusion algorithm to create clusters that are then utilized to create video summaries [51]. [PERSON] et al. utilized multimodal features to develop an approach to segment program boundaries by getting program-oriented informative images (POIM) [52]. They then used these POIMs as basis to get keyframes and representative text to offer visual and textual summaries of the segmented programs. [PERSON] and [PERSON] use multimodal features and fuzzy logic-based rule set to extract highlights of soccer games [53]. A comprehensive multimodal feature extraction can be seen in [PERSON] et al. [54]. In which authors take advantage of all three visual, audio and linguistic modalities and different data fusion techniques to create video summaries. The idea of multimodal fusion is further investigated by [55] while [56] utilize deep learning methodologies with different levels of explicit super-vision for learning attention maps for generating image description.  
The aim of video summarization is essentially to create a new video artifact from the source which is shorter in length. In video summarization, the applied methodologies always choose the content to be included in the output artifact autonomously. The user is not part of the decision-making process and is only shown the final output. This makes it different from the proposed approach because it aims to present the multimodal content extracted from the source to the user and empowers him/her to choose the content they need either to consume immediately or to create a custom multimodal artifact of their choice for later consumption.