# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 8 Quantitative Comparisons
* The CLIP-It method is a very good choice for creating supervised personalized video summarization, as no other method has competitive F-score values on both datasets. Based on the F-score results in Tables 5-8, we draw the following conclusions: The CLIP-It method is a good choice for creating an unsupervised personalized video summary and even better for creating a supervised personalized video summary. In each dataset, better results are provided in the F-score for the supervised setting than for the unsupervised setting. The superiority of the F-score in the supervised setting is due to the use of three loss functions (reconstruction, diversity, and classification) in the model training, in contrast to the unsupervised setting that uses only two loss functions (reconstruction and diversity). Finally, contrastive learning outperforms multimodal language-guided transformation in unsupervised video summarization. Therefore, a better choice seems to be the CSUM-MSVA method for unsupervised personalized video summarization, while the CLIP-It method enables a good summarization. RNNs and CNNs, when integrated into neural networks, can greatly contribute to the production of high-quality personalized video summaries. * The advancement of current techniques and the invention of new methods in unsupervised personalized video summarization call for increased research efforts, given that supervised summarization methods frequently require extensive datasets with human annotations. * Machine learning methods tend to outperform conventional techniques, given their enhanced ability to extract efficient features. In conclusion, it is seen without a doubt that personalized video summarization techniques are very useful in many applications, such as those we mentioned in Section 2 of this paper. Concerning the future directions of personalized video summarization techniques, the goals of future works on personalized video summarization and given the current technology should be the following:  
* For a personalized summary to be more effective, it is crucial to incorporate user preferences extensively. Merely extracting images from the Web is not enough, but there is a need to pull out additional data like user preferences, status, tags, shares, etc. To accomplish this data extraction, the use of advanced joint vision language models along with the application of multimodal information is a necessity in the future. * The advancement of convolution-oriented deep learning techniques for video summarization in image classification, aimed at conceptual cluster labeling, enhances the quality of summarization while maintaining the precision of image classification. Event detection precision can be significantly improved by developing more intricate and expansive structures with the use of deep neural networks. * Investigating the potential of architectures using CNNs to improve run-time performance, as query-based summarization methods suffer from an accuracy versus efficiency trade-off. * Exploring the capacity of query-driven methods for summarization to include the extent to which a user retains an image, entropy, and user focus grounded on visual consistency. * Further research in summarization techniques that utilize the open problem of audio from a clip to identify objects, subjects, or events, for instance, in sports footage, locating scoring occurrences, or identifying a suspect in surveillance footage. The user's speech can also be considered to gather additional information to enhance the video summary. * The development of personalized unsupervised video summarization approaches that incorporate visual language into the multitask learning framework to accurately map the relationship between query and visual content. * The absence of a uniform approach to evaluate produced summaries using standard datasets is a concern, as each study may employ its own evaluation technique and dataset.