# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 5 Experiment and Discussion
### Results and Analysis
#### 5.3.2 Validation of the HybridSumm Model
The abstractive summarization methods, though capable of transcending text structures to generate new sentences through semantic reorganization, are constrained by semantic drift risks (e.g., long-range dependency bias of the Transformer decoders) and high data dependency, resulting in suboptimal performance compared to the hybrid method. The extractive summarization methods, confined to text structures, face information density bottlenecks and logical discontinuity issues, ultimately exhibiting the weakest adaptability and lowest metrics, and thus ranked last among the three groups of methods. 2. Among the four extractive summarization methods, M4 shows significant superiority over M1, M2, and M3. This result strongly proves the effectiveness of our proposed extractive method M4. Specifically, M4 employs the RoBERTa-www-ext model to obtain sentence embeddings, utilizes stacked DCNNs for sentence classification, and further optimizes the architecture through GLUs and residual mechanisms. This innovative combination enables the model to comprehensively capture global contextual semantics in long-text maritime judgment documents, thus generating high-quality summaries. 3. Between the two abstractive summarization methods, M6 shows moderately superior performance compared to M5, with AF\({}_{1}\)-1, AF\({}_{1}\)-2, and AF\({}_{1}\)-L metrics improving by 1.77%, 2.59%, and 2.69%, respectively. This result validates the effectiveness of our proposed abstractive method M6 for Chinese maritime judgment document summarization. Specifically, by integrating the Nezha model, the UniLM model, the \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **M3** & **M4** & **M5** & **M6** \\ \hline batch\_size & 32 & 64 & 3 & 4 \\ epochs & 30 & 20 & 40 & 50 \\ max\_length & 512 & 512 & 512 & 512 \\ hidden\_size & 768 & 384 & 768 & 768 \\ learning\_rate & \(5\times 10^{-5}\) & 0.001 & \(5\times 10^{-5}\) & \(2\times 10^{-5}\) \\ warmup\_steps & 200 & — & 1000 & — \\ dropout\_rate & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for baseline algorithms M3–M6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Method** & **AF\({}_{1}\)-1\%** & **AF\({}_{1}\)-2\%** & **AF\({}_{1}\)-L\%** \\ \hline \multirow{4}{*}{Extractive} & M1 & 34.31 & 16.54 & 28.81 \\  & M2 & 38.31 & 21.29 & 33.59 \\ \cline{1-1}  & M3 & 36.75 & 21.20 & 33.46 \\ \cline{1-1}  & M4 & 53.88 & 39.88 & 52.14 \\ \hline \multirow{2}{*}{Abstractive} & M5 & 57.70 & 41.98 & 54.32 \\ \cline{1-1}  & M6 & 59.47 & 44.57 & 57.01 \\ \hline Hybrid & M7 & 69.95 & 59.64 & 68.66 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of M1–M7 using ROUGE AF\({}_{1}\)-N and AF\({}_{1}\)-L.  
copy mechanism from a pointer-generator network, the coverage mechanism, and beam search, M6 can effectively capture domain-specific semantic patterns while maintaining structural coherence, thus exhibiting good performance. 4. The hybrid summarization method M7, which integrates extractive method M4 and abstractive method M6, outperforms both individual methods. Quantitative analysis shows that M7 achieves improvements in AF\({}_{1}\)-1, AF\({}_{1}\)-2, and AF\({}_{1}\)-L by 16.07%, 19.76%, and 16.52%, respectively, compared to M4, and by 10.48%, 15.07%, and 11.65%, respectively, compared to M6. This demonstrates that in the task of maritime judgment document summarization, using the extractive summaries generated by M4 as the input to M6 can effectively overcome the limitations of individual methods, and significantly enhance summary quality. 5. Among the seven summarization methods (M1-M7), the hybrid method M7 proposed in this paper demonstrates superior performance in generating summaries for Chinese maritime judgment documents. This result strongly validates the effectiveness of M7 and highlights its exceptional capability in the task of maritime judgment document summarization. In addition, the following points can also be determined from Table 5.
1.