# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 1 Introduction
Hereafter, a rough classification of the current research is presented:  
* Many works were carried out that produced a personalized video summary in real time. [PERSON] and [PERSON] (2010) [17] introduced an application for interactive video summarization in real time on the fly. [PERSON] and [PERSON] (2010) [18] produced personalized basketball video summaries in real time from data from multiple streams. * Several works were based on queries. [PERSON] et al. (2015) [19] proposed a system to create personalized movie summaries with a query interface. Given a given semantic query, [PERSON] (2016) [20] proposed a system that can find relevant digital memories  
Figure 1: Thenumber of papers per year containing in their title the phrase “video summarization” according to google scholar. and perform personalized summarization. [PERSON] and [PERSON] (2020) [21] created a dataset based on a query-video pair. * In a few works, information was extracted from humans using sensors. [PERSON] et al. (2011) [22] used eye gaze and pupillary dilation to generate storyboard personalized video summaries. [PERSON] et al. (2019) [23] used electroencephalography to detect viewer emotion. * Few studies focused on egocentric personalized video summarization. [PERSON] et al. (2015) [24] proposed a personalized egocentric video summarization framework. The most recent study by [PERSON] et al. (2021) [25] presented a framework of unsupervised reinforcement learning in davlong egocentric videos. * Many studies have been conducted in which machine learning has been used in the development of a video summarization technique. [PERSON] and [PERSON] (2011) [26] proposed the summarization of personalized live video logs from a multicamera system using machine learning. [PERSON] et al. (2011) [27] proposed personalized video summarization by supervised machine learning using a classifier. [PERSON] et al. (2019) [28] used the deep CNN model to recognize facial expression. [PERSON] et al. (2019) [29] proposed a Character-Oriented Video Summarization framework. [PERSON] et al. (2021) [30] proposed a triplet deep-ranking model for personalized video summarization. [PERSON] et al. (2022) [31] proposed a framework for personalized video summarization using 2D CNN. [PERSON] and [PERSON] (2022) [32] used Affective Visual Information for Human-Centric Video Summarization. [PERSON] et al. (2022) [33] presented Object of Interest (OoI), a personalized video summarization framework based on the Object of Interest. Figure 2 presents milestone works in personalized video summarization. This figure also shows the first paper published for the personalized video summary and the long time it took for the works to begin to be published en masse. The remainder of this paper is structured as follows. Section 2 introduces the main applications of the personalized video summary. Section 3 presents approaches to summarize personalized video summaries by separating audiovisual cues. Section 4 presents the classification of personalized video summary techniques into six categories according to the type of personalized summary, criteria, video domain, source of information, time of summarization, and machine learning technique. Section 5 classifies the techniques into five main categories according to the type of methodology used by the personalized video summarization techniques. Section 6 describes the individualized video datasets suitable  
Figure 2: Milestones in personalized video summarization ([12] (2001), [14] (2008), [19; 24] (2015), [9; 21] (2020), [25; 30; 34] (2021), [31; 32; 33] (2022)). for summarization. Section 7 describes the evaluation of personalized video summarization methods. In Section 8, the quantitative comparison of personalized video summarization approaches is presented on the most prevalent datasets. In Section 9, this work is concluded by briefly describing the main results of the study conducted. In addition, opportunities and challenges in the field and suggested innovative research lines are analyzed.