# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 2 Related Work
### Extractive Summarization
[PERSON] and [PERSON] [15] proposed a weighted TextRank algorithm for Chinese single-document summarization by leveraging Word2 Vec embeddings to generate sentence vectors. Their approach constructs inter-sentence influence weights through three criteria: cosine similarity between sentences, keyword coverage, and similarity between sentence and title, significantly improving the quality of generated summaries. [PERSON] and [PERSON] [16] developed an improved Chinese single-document summarization method by integrating the LexRank algorithm with the vector space model (VSM), achieving improved summarization performance. This approach employs LexRank's graph-based centrality to compute inter-sentence similarity while leveraging VSM to measure inter-paragraph similarity. Sentence weights are computed by synthesizing multiple factors, including paragraph salience, sentence centrality, and cue words. [PERSON] et al. [17] introduced a hybrid graph-based method for single-document extractive summarization. This method employs a hybrid similarity function--comprising cosine similarity, Jaccard similarity, word alignment-based similarity, and sliding window-based similarity--to calculate edge weight between sentence nodes. It combines sentence centrality (measured by node degree) with six sentence-level features (title words, sentence length, sentence position, numerical data, thematic words, and inter-sentence similarity) to compute sentence scores. Finally, triangle sub-graphs are constructed to optimize sentence selection for summary generation. Summaries are typically concise. To enhance coverage while minimizing redundancy among selected sentences, researchers proposed constraint-based extractive summarization methods. The maximal marginal relevance (MMR) algorithm [18], a widely used technique for redundancy reduction and query-based summarization, prioritizes sentences that maximize relevance to user queries while minimizing similarity to previously selected summary content. [PERSON] et al. [17] combined sentence scoring with the MMR algorithm during triangle sub-graph-based summary generation to minimize redundancy among extracted sentences. [PERSON] et al. [19] proposed a domain-specific method integrating BERT sentence embeddings, agglomerative clustering algorithms, and MMR to summarize news related to cryptocurrency, blockchain, and decentralized finance. [PERSON] and [PERSON] [20]proposed DivSelect, an optimization-driven sentence selection algorithm for extractive document summarization, which balances sentence salience and diversity by selecting sentences with high salience but dis-similar with each other to form summaries. In the 1990s, with the application of machine learning methods--particularly supervised machine learning--to natural language processing (NLP), researchers proposed machine learning-based extractive summarization methods. These approaches formulate sentence selection for summaries as a binary classification problem, categorizing each sentence as either a summary or non-summary sentence [21]. [PERSON] et al. [22] poineered the use of supervised machine learning algorithms in text summarization by introducing a naive Bayesian classifier that calculates sentence selection probabilities based on features such as sentence length cut-off, fixed phrases, paragraphs, thematic words, and uppercase words. Their method generates summaries of specified lengths by extracting sentences ranked by these probabilities. This work demonstrated machine learning's potential for extractive summarization and established foundational principles for subsequent research in machine learning-based approaches. [PERSON] et al. [23] developed a summarization framework for broadcast news and lecture speech by employing different feature combinations--acoustic, structural, and lexical features--to represent sentences. They utilized a support vector machine (SVM) classifier to assess sentence inclusion likelihood based on these combined representations.