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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MDPI AG
2025-05-01
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| author | Lin Zhang Yanan Li Hongyu Zhang |
| author_facet | Lin Zhang Yanan Li Hongyu Zhang |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d3da741d094b4ef9bf167cdddf150bcf |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d3da741d094b4ef9bf167cdddf150bcf2025-08-20T03:47:48ZengMDPI AGApplied Sciences2076-34172025-05-011510543410.3390/app15105434Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment DocumentsLin Zhang0Yanan Li1Hongyu Zhang2School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaIn 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.https://www.mdpi.com/2076-3417/15/10/5434maritime judgment lexiconChinese maritime judgment documentsextractive summarizationabstractive summarizationHybridSumm |
| spellingShingle | Lin Zhang Yanan Li Hongyu Zhang Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents Applied Sciences maritime judgment lexicon Chinese maritime judgment documents extractive summarization abstractive summarization HybridSumm |
| title | Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents |
| title_full | Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents |
| title_fullStr | Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents |
| title_full_unstemmed | Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents |
| title_short | Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents |
| title_sort | deep learning based automatic summarization of chinese maritime judgment documents |
| topic | maritime judgment lexicon Chinese maritime judgment documents extractive summarization abstractive summarization HybridSumm |
| url | https://www.mdpi.com/2076-3417/15/10/5434 |
| work_keys_str_mv | AT linzhang deeplearningbasedautomaticsummarizationofchinesemaritimejudgmentdocuments AT yananli deeplearningbasedautomaticsummarizationofchinesemaritimejudgmentdocuments AT hongyuzhang deeplearningbasedautomaticsummarizationofchinesemaritimejudgmentdocuments |