# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 1 Introduction  
In recent years, the rapid advancement of technology has led to the integration of camcorders into many devices. As the number of camcorders increases, so does the number of recorded videos [1]. This results in a huge increase in videos that are uploaded daily on the Internet [2]. Daily activities or special moments are recorded, resulting in large amounts of video [3]. Human access to multimedia creation, mainly through mobile devices and the tendency to share them through social networks [3], causes an explosion of videos available on the Web. Searching for specific video content and categorization is generally time-consuming. The traditional representation of video files as a sequence of numerous consecutive frames, each of which corresponds to a constant time interval, while adequate for viewing a file in a movie mode, presents a number of limitations for the new emerging multimedia services such as content-based search, retrieval, navigation, and video browsing [4]. The need for rational time management led to the development of automatic video content summarization [2] and indexing/clustering [1] techniques to facilitate access, content search, and automatic categorization (tagging/labeling), as well as action recognition [5] and common action detection in videos [6]. The number of papers per year that contain in their title the phrase \"video summarization\" according to Google Scholar is depicted in Figure 1.  
Many studies have been devoted to developing and designing tools that have the ability to create videos with a duration shorter than the original video, reflecting the mostimportant visual and semantic content [4; 7]. As users grow in the base, so does the diversity between them. A summary which is uniform for all users may not suit everyone's needs. Each user can consider different important sections according to his interests, needs, and the time he will spend. Therefore, the focus should be on the personalized summary of the general video summary [8]. User preferences, which are often involved in the expected results, should be taken into account in video summaries [9]. Therefore, it is important to modify the video summary to suit the user's interests and preferences, thus creating a personalized video summary, while retaining important semantic content from the original video [10]. Video summaries that reflect the understanding of individual users about the content of the video should be personalized in a way that is based on individual needs and intuitive. Consequently, the personalized video summary is tailored to the individual user's understanding of the video content or understanding of the content of the video. In contrast, video summaries that are not personalized are not customized in any way to the understanding of the individual user. In several ways, summaries can be personalized. For example, personalizing video summaries can involve pre-filtering content via user profiles before showing it to the user, creating summaries tailored to the user's behavior and movements during video capture, and customizing summaries according to the user's unique browsing and access habits [11]. Several studies on personalized video summarization have appeared in the literature. In [12], [PERSON] et al. (2001) introduced personalized video summarization for mobile devices. After two years, [PERSON] et al. (2003) [13] used context information to generate hierarchical video summarization. [PERSON] and [PERSON] (2008) [14] proposed a personalized summary video framework from the semantic extraction features of each frame. [PERSON] and [PERSON] (2012) [15] proposed a personalized system for video summarization and retrieval. [PERSON] et al. (2013) [16] proposed a personalized video summarization that is interactive and based on sketches. [PERSON] et al. (2020) [9] provided personalized video summarization through a recommender system, where the output is personalized rankings of video segments.