# 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  
To validate the effectiveness of the proposed HybridSumm model for maritime judgment document summarization, we conduct a comprehensive comparison among seven summarization algorithms, six of which are commonly used baseline algorithms, and one is the HybridSumm model. 1. M1: the LexRank algorithm [14]. 2. M2: the Word2 Vec-based LexRank algorithm. In contrast to M1, which employs TF-IDF-based VSM for sentence vectorization, M2 constructs contextualized sentence representations through Word2 Vec embeddings. Consider a sentence \(s\) with \(q\) feature terms. Let \(\mathbf{e}_{i}\in\mathbb{R}^{d}\) (\(1\leq i\leq q\)) represent the \(d\)-dimensional embedding of the \(i\)-th term. The corresponding sentence vector \(\mathbf{v}_{s}\) is computed as the arithmetic mean of all \(q\) term embeddings, as shown in Equation (18). The embeddings are generated by a 300-dimensional Word2 Vec model trained on a 9.86 GB Chinese Wikipedia corpus using the Gensim library. \[\mathbf{v}_{s}=\frac{\sum_{i=1}^{q}\mathbf{e}_{i}}{q}\] (18)
3. M3: the MacBERT-attention algorithm. MacBERT [72], developed by HFL in November 2020, is a BERT-based Chinese pre-trained language model. In this paper, the base version of the MacBERT model [73] with a multi-head attention mechanism is employed to summarize maritime judgment documents. Specifically, based on the attention matrices produced by each head in the last layer of MacBERT's multi-head attention mechanism, the average attention scores of all heads for each token within the input sequence are computed, with the score of the [CLS] token taken as the sentence score. Subsequently, the top-scoring sentences are extracted to form a summary. 4. M4: the RoBERTa-DCNN-GR algorithm, which is based on the maritime judgment lexicon, as proposed in Section 4.1. 5. M5: the Chinese BART model, a Chinese-specific linguistically enhanced adaptation of the BART model [74] originally developed by Facebook AI in 2019. By combining BERT's bidirectional contextual encoding with GPT-style autoregressive decoding, it achieves exceptional performance in both text generation and comprehension tasks. In this paper, we employ the base version of the Chinese BART [75], fine-tuned on a dataset of 1500 documents, to generate summaries of maritime judgment documents. 6. M6: the Nezha-UniLM-CC algorithm, which is based on the maritime judgment lexicon, as proposed in Section 4.2. 7. M7: the HybridSumm algorithm, which is based on the maritime judgment lexicon, as proposed in Section 4.3. Unlike M6 that takes raw maritime judgment documents as input, M7 uses the pre-summary generated by M4 as the input for M6, and then utilizes M6 to produce the final summary. The above seven algorithms are categorized into three groups: M1-M4 as extractive summarization algorithms; M5-M6 as abstractive summarization algorithms; and M7 as a hybrid summarization algorithm. The parameter settings for baseline algorithms M3-M6 are shown in Table 4.  
To objectively quantify their performance, the ROUGE-based automatic metrics AF1-N (Equation (10)) and AF1-L (Equation (13)) are employed to evaluate the quality of summaries generated from the test set of maritime judgment documents. The comparison results are presented in Table 5.  
The following points can be determined from Table 5.  
1. Among the three groups of summarization methods, the hybrid method exhibits the best performance, followed by the abstractive methods, while the extractive methods rank last. This is mainly due to the differences in their capabilities for deep semantic understanding and information restructuring mechanisms. The hybrid summarization method integrates extraction and generation techniques through a two-stage processing framework (first extracting key sentences, then performing semantic reconstruction) and dynamic balancing mechanisms (e.g., attention mechanisms). This ensures the accuracy of facts while improving linguistic fluency, thus producing the highest quality summaries.