# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### RoBERTa-DCNN-GR: An Extractive Summarization Model
#### 4.1.3 Dilated Convolutional Neural Network (DCNN)  
CNN is a type of feed-forward neural network typically composed of alternately stacked convolutional layers, pooling layers, and fully connected layers. Its core strength lies in extracting local features through convolutional operations and compressing feature dimensions via pooling to enhance generalization, while optimizing network weights through backpropagation. However, CNN exhibits limitations when processing long text sequences: first, the limited receptive field of traditional convolutions struggles to model global information; second, pooling operations may discard critical details during dimensionality reduction, weakening the ability to capture long-range dependencies.  
To address this issue, [PERSON] and [PERSON] [63] proposed DCNN in 2016. Its core innovation involves introducing a dilation rate to expand the receptive field of convolutional kernels without increasing the number of parameters. Specifically, the dilation rate controls the spacing between convolutional kernel elements (rather than directly adjusting kernel size), enabling the exponential expansion of the receptive field (e.g., stacking multiple dilated convolutions progressively enlarges coverage). This design significantly enhances the model's capability to capture long-range contextual features while avoiding information loss from pooling operations, thereby more efficiently extracting multi-level textual information.  
Figure 4 illustrates the comparison between CNN and DCNN, both with 3 \(\times\) 3 kernels. In this figure, yellow nodes represent the input layer, orange represent denote the hidden layers, green nodes represent the output layer, and the direction of the arrows illustrates the flow path of information through the network. As can be seen from Figure 4:  
1. In the CNN architecture, each of the three convolutional layers employs a 3 \(\times\) 3 kernel, where the output features are computed from a local 3 \(\times\) 3 region of the previous layer. By stacking these three layers, the final receptive field expands to 7 (calculated as \(1+(3-1)\times 3=7\)), enabling the model to capture contextual information from 7 consecutive characters in the input text. However, this limited receptive field restricts its ability to model long-range dependencies in long sequences.
2. In the DCNN architecture, the first layer uses a 3 \(\times\) 3 kernel with a dilation rate of 1 (equivalent to standard convolution), yielding a receptive field of 3. The second layer applies a dilation rate of 2, effectively covering a 5 \(\times\) 5 region (calcu  
Figure 4: Receptive field expansion in CNN vs. DCNN with 3 \(\times\) 3 kernels: layer-wise contextual span visualization.  
lated as \(1+(3-1)\times 2=5\)). The third layer further scales the dilation rate to 4, expanding coverage to a 9 \(\times\) 9 region (calculated as \(1+(3-1)\times 4=9\)). By stacking these layers, DCNN achieves a final receptive field of 15 (calculated as \(1+(3-1)\times(1+2+4)\ =\ 15\)), enabling the capture of global dependencies across 15 characters in the input text.
3. DCNN expands the receptive field by introducing dilated spacing between kernel elements instead of increasing kernel size, and it maintains the same number of parameters as CNN (with nine weights per layer). This design enables the efficient capture of long-range features, allowing DCNN to significantly outperform CNN in processing long texts, such as summarizing maritime judgment documents.