## 2 Related Works
### Arabic Text Classification (ATC)
Data preprocessing and augmentation were not used in their scenario. To assess their model, eight large-scale datasets (same as in [PERSON]) [51] are used, and their very deep model (i.e., 29 convolutional layer depths) outperforms the ConvNet proposed by [PERSON] [51]. They found that the text classification performance was slightly increased when their proposed model became deeper (i.e., 29 convolutional layers). After that, they noticed that the performance was again decreased with 49 convolutional layers. In [38], [PERSON] et al. proposed the modified version of the VDCNN, which was presented by [PERSON] et al. [18] and called it a squeezed very deep CNN (SVDCNN) for English text classification. Basically, their idea was to reduce the number of standard convolutional blocks used in VDCNN. In particular, the method reduced the trainable parameters and minimized the need for a high storage capacity. To do that, they modified the standard convolutional blocks of VDCNN by using temporal depthwise separable convolutions (TDSCs). Meanwhile, since 90% of the trainable parameters in a specific deep learning model are always created due to the number of dense layers [25; 26], they replaced them with global average pooling (GAP) layers. In 2020, [PERSON] et al. proposed the first deep learning model called CE-CLCNN for Arabic documents classification [18]. The deep learning CE-CLCNN model consists of two parts: a character autoencoder to encode the image-based character embedding and a character-level CNN (CLCNN) for classification purposes. It encodes each Arabic character or alphabet as a 2D image. Then, the Arabic text is represented as an array of character-based images. To handle the class imbalance problem (i.e., long-tailed data distribution problem), they used a class-balanced loss function. To evaluate their model, they created their own two datasets called the Arabic Wikipedia title (AWT) dataset and the Arabic poetry dataset (APD). The proposed model showed much classification improvement against the classical SVM machine learning classifiers by 21.23% and 4.02% in terms of the micro F1-score for the ADP and AWT datasets, respectively. In 2020, [PERSON] et al. proposed the first deep learning structure via image-based character for Arabic documents classification, which was called AraDIC [20]. In this approach, they represented each Arabic character or alphabet as a 2D image. The proposed deep learning AraDIC consists of a 2D image-based character encoder and classifier. The character encoder is a simple CNN that consists of three 2D convolutional layers, two max-pooling layers, and two dense layers. In addition, the classifier is also a simple character-level CNN that consists of four 1D convolutional layers, two max-pooling layers, and two dense layers. Batch normalization and the ReLU activation function are used after each convolutional and dense layer. To avoid the imbalance problem, they trained their model end-to-end utilizing the weighted-class loss function. To evaluate their proposed model, they used both AWT and APD datasets. The proposed AraDIC outperforms other classical and deep learning baseline text classification techniques by 12.29% and 23.05% in terms of micro- and macro-F1-scores, respectively. In [2], [PERSON] et al. proposed a combined deep learning model of CNN and RNN for Arabic text documents categorization using static, dynamic, and fine-tuned word embedding. A deep learning CNN model is used to automatically learn the most meaningful representations from the space of Arabic word embedding. The proposed CNN consists of three key components: the convolutional layer, pooling layer, and fully connected or dense layer. To produce a full summary of the input Arabic text, a stacked set of bidirectional gated recurrent units (Bi-GRUs) was used. Then, multiple dense layers were utilized to finally recognize the input Arabic text into the most likely category. They evaluated their proposed deep learning model using an open source Arabic corpora (OSAC) dataset.