# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.1 Supervised
Keyshot-driven video summarization techniques are used to generate excerpts from short videos (such as user-created TikToks and news) or long videos (such as full-length movies and soccer games) [31]. [PERSON] et al. [31] presented the LTC-SUM method to produce personalized keyshot summaries that minimize the distance between the semantic information of the side and the selected video frame. Using a supervised encoder-decoder network, the importance of the frame sequence is measured. [PERSON] et al. [65] proposed a mapping network (MapNet) to express the degree of association of a shot with a given query, to create a visual information mapping in the query space. Using deep reinforcement learning (SummNet), they proposed to build a summarization network to integrate diversity, representativeness, and relevance to produce personalized video summaries. [PERSON] and [PERSON] proposed a scoring mechanism [69]. In the hierarchical structure, the mechanism is based on the scene layer and the shooting layer and receives the output. Each shot is scored through this mechanism, and as basic shots, the shots are selected as high-rated shots. * _Event-based selection:_ The process of personalized summarization detects events from a video based on the user's preferences. In the above method [31], to identify thumbnail events that are not specific domains, a two-dimensional convolutional neural network (2D CNN) model was implemented. * _Learning shot-level features:_ It involves learning advanced semantic information from a video segment. The Convolutional Hierarchical Attention Network (CHAN) method was proposed by [PERSON] et al. [55]. After dividing the video into segments, visual features are extracted using the pre-trained network. To perform shot-level feature learning, visual features are sent to the feature encoding network. To perform learning on a high-level semantic information video segment, they proposed a local self-attention module. To manage the semantic relationship between the given query and all segments, they used a global attention module that responds to the query. To reduce the length of the shot sequence and the dimension of the visual feature, they used a fully convolutional block. * _Transfer learning:_ Transfer learning involves adjusting the information gained from one area (source domain) to address challenges in a separate, yet connected area (target domain). The concept of transfer learning is rooted in the idea that when tackling a problem, we generally rely on the knowledge and experience we have gained from addressing similar issues in the past [76]. [PERSON] et al. [28] proposed a framework using transfer learning to perform facial expression recognition (FER). More specifically, they presented the learning process that, to be completed, includes two steps. In the first step, a CNN model is trained for face recognition. In the second step, transfer learning is performed for the FER of the same model. * _Adversarial learning._ This is a technique employed in the field of machine learning to trick or confuse a model by introducing harmful input, which can be used to carry out an attack or cause a malfunction in a machine learning system. A competitive three-player network was proposed by [PERSON] et al. [64]. The content of the video, as well as the representation of the user query, is learned from the generator. The parser receives three pairs of digests based on the query so that the parser can distinguish the real digest from a random one and a generated one. To train the classifier and the generator, a lossy input of three players is performed. Training avoids the generation of random summaries which are trivial, as the summary results are better learned by the generator. * _Vision-language:_ A vision language model is an artificial intelligence model that integrates natural language and computer vision processing abilities to comprehend and produce textual descriptions of images, thus connecting visual information with natural language explanations. [PERSON] et al.