# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 1 Introduction  
As a major maritime nation with extensive strategic interests, China has prioritized building a strong maritime power as a core national strategy, driven by the advancement of the Belt and Road Initiative and the rapid growth of the modern marine economy. Maritime trial, as a key judicial mechanism to safeguard this strategic development, has increasingly demanded specialized and intelligent updates. In recent years, China has been advancing the digital transformation of its judicial system, emphasizing the integration of cutting-edge technologies like artificial intelligence (AI) into trial processes to enhance efficiency and transparency. Within this context, the intelligent processing of maritime judgment documents has emerged as a critical task requiring urgent breakthroughs. Maritime judgment documents (primarily judgments and rulings) exhibit distinct professional characteristics: excessive length (often spanning dozens of pages), dense legal terminology, and rigorous structure. Traditional manual summarization, plagued by lowefficiency and high costs, struggles to cope with rapidly growing caseloads. Consequently, developing automatic summarization methods for maritime judgment documents has emerged as a critical challenge. Automatic text summarization, a core NLP technology that generates concise and coherent summaries capturing document essentials, has been widely used in news and academic domains. In recent years, this technology has extended to legal text processing, offering innovative pathways for the efficient extraction of legal information. However, existing approaches exhibit two critical limitations: first, they predominantly focus on surface-level linguistic patterns, failing to adequately model deep semantic relationships--such as implicit legal element logic and statutory provision dependencies--in legal documents; second, they are primarily tailored to civil and commercial judgment documents, leaving the specialized domain of maritime judgment documents under-researched. The automatic summarization of maritime judgment documents confronts dual technical barriers: (1) linguistic heterogeneity: judges' individualized writing styles amplify the unstructured nature of legal texts, complicating content parsing and key information extraction; and (2) semantic complexity: high-density domain terminology and complex case details demand advanced capabilities in both discourse-level semantic modeling and legal logical reasoning. To address these challenges, this paper leverages deep learning's prowess in long-text semantic analysis to bridge legal studies and artificial intelligence, exploring intelligent summarization techniques using the publicly available maritime judgment documents. The goal is to enhance judicial information processing efficiency, supporting the Belt and Road Initiative and maritime power strategy. The main contributions of this paper are as follows: (1) to address the complexity and specialization of maritime judgment documents, we constructed a maritime judgment lexicon by extracting terms such as laws, regulations, and international conventions in real legal documents. This lexicon resolves terminological ambiguities caused by conventional tokenization tools and improves term consistency and legal compliance in the generated summaries. (2) Targeting the challenge of lengthy maritime judgments, we propose HybridSumm, a hybrid \"extraction-abstraction\" summarization framework based on the maritime judgment lexicon. The term \"extraction-abstraction\" refers to the framework's integration of the information filtering capability of extractive models with the semantic reorganization advantages of abstractive approaches. Specifically, in the extraction stage, the RoBERTa-DCNN-GR model (see Section 4.1) is employed to select key sentences from lengthy judgment documents.