# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 5 Experiment and Discussion
### Results and Analysis  
#### 5.3.1 Validation of Maritime Judgment Lexicon  
To validate the effectiveness of the maritime judgment lexicon constructed in Section 3.2 in the automatic summarization of maritime judgment documents, this paper conducts a comparison of the quality of summaries generated by the summarization models under two scenarios: with and without the provision of the maritime judgment lexicon, as shown in Table 3. RoBERT-DCNN-GR, Nezha-UniLM-CC, and HybridSumm in Table 3 refer to the extractive summarization model (see Section 4.1), the abstractive summarization model (see Section 4.2), and the hybrid (see Section 4.3) summarization model based on the maritime judgment lexicon, respectively. Moreover, \"--GMJT\" denotes the removal of the maritime judgment lexicon from the aforementioned three models. As shown in Table 3, the absence of the maritime judgment lexicon leads to performance degradation across all three models. Specifically, the AF\({}_{1}\)-1, AF\({}_{1}\)-2 and AF\({}_{1}\)-L metrics of the RoBERT-DCNN-GR model decrease by 1.80%, 1.50%, and 1.61%, respectively; the Nezha-UniLM-CC model exhibits reductions of 3.07%, 4.25%, and 3.22% for the same metrics; while the HybridSumm model shows declines of 1.74%, 3.12%, and 2.07%, respectively. From these results, it can be concluded that:  
1. RoBERTa-DCNN-GR exhibits the lowest dependence on the maritime judgment lexicon, primarily due to its importance-driven extraction strategy. By directly selecting key sentences from the source text, this model prioritizes high-importance content, even if technical terms are incompletely identified. This approach inherently reduces reliance on precise term segmentation, thereby mitigating the impact of segmentation errors. Consequently, the exclusion of the lexicon results in only limited performance degradation. 2. Nezha-UniLM-CC shows the strongest dependence on the lexicon, directly related to its \"pure generative\" architecture design. As an abstractive summarization model, it requires explicit access to domain-specific terminology through a lexicon; otherwise, it is prone to producing ambiguous terms or incorrect citations of laws and regulations. The integration of the lexicon enables the model's pointer-generator network to copy standardized legal terms during decoding, ensuring precise expression of legal concepts. Furthermore, the predefined keywords of laws and regulations in the lexicon standardize the citation logic of legal bases, enabling the model to accurately cite valid legal sources. Consequently, the maritime judgment lexicon has the greatest impact on Nezha-UniLM-CC's performance. 3. HybridSumm demonstrates intermediate dependence on the lexicon, primarily due to its \"extraction-abstraction\" hybrid architecture. The extraction stage preserves key sentences containing domain terms, while the abstraction stage reorganizes content for fluency. Without the lexicon, the abstraction stage still encounters term precision challenges; however, the retained sentences from the extraction stage partially mitigate this issues, resulting in performance declines between purely extractive and purely abstractive models. 4. The maritime judgment lexicon has enhanced the performance of all three models--RoBERT-DCNN-GR, Nezha-UniLM-CC, and HybridSumm--to varying degrees, confirming the non-negligible role of domain-specific terminology in maritime legal summarization. By explicitly encoding professional terms and their semantic relationships, the lexicon provides interpretable domain-specific prior constraints for automatic summarization models, thereby improving the accuracy, coherence, and judicial logical validity of generated summaries.