# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 2 Related Work
### Automatic Summarization of Judgment Documents  
A judgment document, as a legal instrument, requires its summarization to not only extract the core content, but also strictly adhere to the professional and normative standards inherent in legal texts, ensuring the precise representation of legal provisions and uncompromising accuracy of statutory language. Early studies on automatic summarization of judgment documents primarily employed discourse structure-based extractive methods. These approaches first divide a judgment document into several functionally distinct discourse units, then utilize statistical analysis or machine learning algorithms to identify key sentences within each unit. The selected sentences are subsequently recomposed into summaries while preserving the original logical structure [50]. [PERSON] et al. [51] categorized sentences in UK House of Lords judgments into seven rhetorical roles: FACT, PROCEEDINGS, BACKGROUND, PROXIMATION, DISTANCING, FRAMIING, and DISPOSAL. By analyzing linguistic features such as tense, POS, and cue phrases, they identified role-specific sentences for abstractive summary generation. [PERSON] and [PERSON] [52] segmented the discourse structure of judgments from the Federal Court of Canada into four thematic components: INTRODUCTION, CONTEXT, JURIDICAL ANALYSIS, and CONCLUSION. By integrating positional features with TF-IDF weighting, they developed an extraction framework to select theme-specific sentences, ultimately generating table-style summaries that preserve legal discourse coherence. As research continues, a number of scholars believe that legal knowledge plays an important role in the summarization of legal documents. [PERSON] et al. [53] developed a knowledge base comprising 23 manually crafted rules and integrated it with automatic summarization techniques to generate summaries for case reports from the Australasian Legal Information Institute (AustLII). [PERSON] et al. [54] proposed a controversy focus-based extractive summarization method for civil trial court debate records. They found that controversy focuses constitute the core arguments between parties from different camps (plaintiffs, defendants, witness, judge, etc.) during trials, with debate dialogues organized around these controversy focuses. To generate summaries, their method first categorizes sentences in debate records based on their alignment with identified controversial focuses, and then selects salient sentences from each category to construct summaries. [PERSON] et al. [55] proposed DELSumm, an unsupervised extractive summarization algorithm for legal case documents from the Indian Supreme Court. The method incorporates domain knowledge from legal experts to assign weights to thematic segments, sentences, and content words within case documents. Using integer linear programming (ILP), DELSumm optimizes sentence selection for maximal informal coverage while integrating representations from all rhetorical segments to minimize redundancy in generated summaries. With the rapid advancement of AI technology in recent years, researchers have begun employing deep learning methods to perform automatic summarization of legal documents. At the 2020 Legal AI Challenge (LAIC2020), [PERSON] et al. [56] proposed a BERT-based hybrid summarization model integrating extractive and abstractive approaches. The model achieved the highest ROUGE score of 61.43% on the LAIC2020 dataset, which comprises 9848 first-instance civil judgment documents. [PERSON] et al. [57] proposed BASR, an abstractive summarization model that integrates BERT, attention mechanisms, and reinforcement learning, tailored for civil judgment documents related to small claims cases and summary procedures. [PERSON] and [PERSON] [58] proposed an extractive summarization method for judgment documents from Indian Supreme Court. They labeled the importance of body sentences (1 for important, 0 for unimportant) by computing their similarity to head-note sentences.