# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents  
Based on the maritime judgment lexicon constructed in Section 3.2, the extractive summarization model from Section 4.1, and the abstractive summarization model from Section 4.2, this paper proposes HybridSumm, a hybrid summarization model that integrates the maritime judgment lexicon, as illustrated in Figure 5.  
The model adopts an \"extraction-abstraction\" two-stage collaborative architecture, with the maritime judgment lexicon as its core foundation, and achieves efficient summarization through the following mechanisms:  
Extraction stage: to address the lengthy nature of maritime judgment documents, the RoBERTa-wwm-ext pre-trained model is employed for sentence-level vector encoding, constructing high-dimensional sentence representations via average pooling. A multi-level feature processing framework is designed to capture key semantic features in long texts: First, DCNNs are utilized to capture long-distance dependencies in text sequences, leveraging their expanded receptive fields to process extended semantic correlations. Then, GLUs are employed to perform feature filtering, combined with residual connections for information enhancement. Finally, a deep feature space is constructed by stacking multi-layer DCNNs to classify key sentences versus non-key sentences. Through this stage, the concatenation of key sentences forms an extractive summary, achieving preliminary semantic compression from verbose text to concise summary.  
Abstraction stage: the extractive summary serves as input to a Nezha-based encoder for constructing contextualized embeddings, while the sequence generation task is fined-tuned through a UniLM-adapted decoder architecture. To enhance generation quality, this paper introduces a dual-mechanism collaborative optimization strategy. On the one hand, through the copy mechanism of a pointer-generator network, the vocabulary is dynamically expanded to alleviate OOV issues with domain-specific terminology, ensuring accurate generation of maritime legal terms. On the other hand, the coverage mechanism is employed to track historical attention distributions, suppressing repetitive word generation through attention weight decay. This dual guarantee mechanism achieves complementary  
Figure 5: HybridSumm architecture: maritime judgment summarization via extractive–abstractive pipeline with maritime domain-specific lexicon integration. The arrows represent the flow and processing direction of information or data.  
optimization in domain adaptability and linguistic fluency, significantly improving the professionalism and readability of generated summaries.  
The HybridSumm model achieves triple synergistic advantages through the organic integration of the maritime judgment lexicon and the hybrid architecture: ensuring accurate expression of domain-specific terminology via specialized lexicon, preserving key legal elements through the extractive model, and improving linguistic fluency and structural standardization via the abstractive model. Addressing three core dimensions--semantic accuracy, content completeness, and expression standardization--HybridSumm comprehensively enhances the quality and efficiency of automatic summarization of maritime judgment documents, providing an innovative solution for intelligent processing of judicial texts.