# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 8 Quantitative Comparisons  
In this section, we present quantitative comparisons of personalized video summarization approaches on the most prevalent datasets in the literature, which are TVSum and SumMe. The evaluation of unsupervised personalized video summarization methods using the F-score in the SumMe dataset is presented in Table 5. Table 6 shows the performance of the personalized unsupervised video summarization methods in the TVSum dataset evaluated using the F-score. Based on the F-score results in Tables 5 and 6, the following observations are worth mentioning. The video features a woman explaining the process of making a chicken sandwich. The ground truth summary presents scores derived by averaging user annotations and highlights the keyframes that scored highly. Subsequently, the figure displays outcomes from the baseline CLIP-Image+Transformer, which relies solely on visual cues without linguistic input. The scoring indicates that frames highly rated in the ground truth also score well in the baseline; however, numerous irrelevant frames also score highly. Consequently, the model selects keyframes where the individual is either speaking or consuming the sandwich (marked in red), which are not representative of the crucial steps in the video. Introducing language through generated captions mitigates this issue. The final row depicts captions created by the bi-modal transformer (BMT) [107]. These results, from the complete CLIP-It model, show predicted scores aligning more closely with the ground truth, with the highest scoring keyframes matching those of the ground truth. * The multimodal Multigraph Fusion (MGF) method, introduced in the QUASC approach to automatically identify events that are related to the query based on the tag information in the video, such as descriptions and titles, does not allow one to create a good summary. As a result, the QUASC approach is not competitive with any other approach in the TVSum dataset. * The Actor-Critic method allows the creation of a satisfactory personalized video summary, which is shown by the F-score in the two datasets. To capture spatio-temporal features, 3D convolutional neural networks (CNNs) are used. In addition, a bi-directional short-term memory network (BiLSTM) is used to learn the extracted features. However, the Actor-Critic method is not competitive with the pioneering CSUM-MSVA and CLIP-It method. Table 7 shows the performance of the supervised personalized video summarization methods evaluated using the F-score in the SumMe dataset. Table 8 refers to the performance of the personalized supervised video summarization methods in the TVSum dataset  
Figure 11: Comparison of the reference summary with the outcomes obtained from the CLIP-Image + Transformer and the complete CLIP-It model (CLIP-Image + Video Caption + Transformer). The content provided is a video demonstrating a cooking procedure. Without captions, the model gives high scores to some irrelevant frames, like those showing the woman talking or eating, which negatively impacts the precision. By including captions, the cross-attention mechanism guarantees that frames featuring significant actions and objects receive elevated scores [58]. evaluated by the F-score. Based on the F-score results in Tables 7 and 8, the following observations are worth mentioning. * The use of structural and optical information labeling in the method of [PERSON] et al. [54] does not appear to allow the production of a good personalized supervised summary, as it provides satisfactory results that are competitive only with the method of [PERSON] et al. [78] in the SumMe dataset. * Using the reinforced attentive description generator and the query-aware scoring module(QSM) does not seem to help, as [PERSON] et al. 's [78] method is not competitive compared to the CLIP-It method on both datasets. * Balanced performance seems to be given by the [78] method of [PERSON] et al. and by the CLIP-It method, as neither seems to be adapted to a single dataset.