Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
In the context of China’s accelerating maritime judicial digitization, automatic summarization of lengthy and terminology-rich judgment documents has become a critical need for improving legal efficiency. Focusing on the task of automatic summarization for Chinese maritime judgment documents, we pro...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5434 |
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| Summary: | In the context of China’s accelerating maritime judicial digitization, automatic summarization of lengthy and terminology-rich judgment documents has become a critical need for improving legal efficiency. Focusing on the task of automatic summarization for Chinese maritime judgment documents, we propose HybridSumm, an “extraction–abstraction” hybrid summarization framework that integrates a maritime judgment lexicon to address the unique characteristics of maritime legal texts, including their extended length and dense domain-specific terminology. First, we construct a specialized maritime judgment lexicon to enhance the accuracy of legal term identification, specifically targeting the complexity of maritime terminology. Second, for long-text processing, we design an extractive summarization model that integrates the RoBERTa-wwm-ext pre-trained model with dilated convolutional networks and residual mechanisms. It can efficiently identify key sentences by capturing both local semantic features and global contextual relationships in lengthy judgments. Finally, the abstraction stage employs a Nezha-UniLM encoder–decoder architecture, augmented with a pointer–generator network (for out-of-vocabulary term handling) and a coverage mechanism (to reduce redundancy), ensuring that summaries are logically coherent and legally standardized. Experimental results show that HybridSumm’s lexicon-guided two-stage framework significantly enhances the standardization of legal terminology and semantic coherence in long-text summaries, validating its practical value in advancing judicial intelligence development. |
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| ISSN: | 2076-3417 |