###### Abstract
The efforts made to handle the challenge of video summarization are outlined in this section, with a focus on deep-learning-based approaches. The authors of [13] took advantage of spatio-temporal learning with 3D-CNNs, LSTMs, and recurrent neural networks to detect soccer video highlights. A GAN-based framework was presented in [14] with an attention-aware Ptr-Net generator and a 3D-CNN discriminator. HEVC intra-frame coding was leveraged by the authors of [15] via merging weighted luminance and chrominance values with a texture-based feature against a threshold to group frames into a video summary. In [16], a stacked memory network (SMN) with LSTM layers was presented that models long dependencies among frames to decrease redundancy in the final summaries. A framework presented in [17] focuses on cost-sensitive learning by having a spatial stream that represents the appearance of frames and a temporal stream that uses motion vectors to represent the temporal information of a video. The authors of [18] built an unsupervised GAN with an attention mechanism to detect meaningful parts of a video. In [19], motion information between frames is leveraged as spatio-temporal information is extracted and inter-frame motion is generated from it, and a self-attention model selects key frames for the summary. Multi-video summarization was explored by the authors of [20], who applied target-appearance-based shot segmentation in addition to feature extraction from frames; these features were passed to a bidirectional LSTM to generate probabilities to form a summary. An attentive encoder-decoder network was presented by the authors of [21], in which they used a bidirectional LSTM as the encoder to extract contextual information between frames and then two attention-based LSTM networks as the decoder, which used additive and multiplicative objective functions. An encoder-decoder CNN structure was developed by the authors of [22], who used a diagnostic view plane detection network as the encoder, followed by a decoder that feeds features into a bidirectional LSTM to analyze the features of preceding and future frames. The final reinforcement learning network selected key frames for the summary. Video summarization was achieved by the authors of [23] in the Internet of Things (IoT) domain by developing a CNN for shot segmentation and image memorability, using aesthetic- and entropy-based features to ensure summary variation. The work in [24] used motion information and a clustering validity index to segment shots and select key frames by estimating their forward and backward motion. A self-attention binary neural tree (SABT-Net) model was presented in [25], in which the authors used GoogleNet for feature extraction in addition to shot encoding, branch routing, self-attention, and score prediction modules to achieve video summarization. The authors of [26] used a sparse autoencoder to combine feature vectors derived from multiple  
Figure 1: General overview of the system architecture. Feature extraction is performed through HEVC coding. Temporal subsampling using HEVC features, PCA, or cosine similarity. The reduction of the feature space is optional with stepwise regression. Training is performed with LSTM networks, 1D-CNNs, or random forests. pre-trained CNNs into a reduced space with a random forest classifier to form video summaries. A TTH-RNN was presented in [27] and comprised a tensor train embedding layer with a hierarchical LSTM to capture forward and backward temporal intra-shot dependencies and encoded inter-shot dependencies to establish the importance of each frame and form a final summary. The authors of [28] offer CLIP-It, a framework for dealing with query-focused video summarization through the use of a multimodal transformers that correlates frames with user-written queries. The authors of [29] proposed a deep hierarchical LSTM with attention for video summarization (DHAVS) in response to the LSTM's inability to handle longer video sequences.