# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 2 Related Work
### Extractive Summarization  
Extractive summarization methods treat sentences as fundamental processing units, rank them through salience scoring mechanisms, and compile the top-ranked candidates into a summary [1]. Early studies on extractive summarization primarily employed statistical approaches that utilized features such as term frequency, sentence position, cue words, and document titles to compute sentence importance [3]. LEAD-3--one of the earliest statistical feature-based extractive methods--generates summaries by extracting the first three sentences of a document. Despite its simplicity, LEAD-3 demonstrates high effectiveness for news article summarization, where the opening sentences typically contain the most salient information [4]. [PERSON] [5] expanded this paradigm by scoring sentences using four statistical criteria: cue words, key words, title and heading words, and sentence location, then selecting top-ranked sentences to form summaries. [PERSON] and [PERSON] [6] introduced sentence weighting metrics combining term frequency, noun phrase length, and content word frequency within noun phrases, achieving competitive performance in news document summarization. Building on these works, [PERSON] et al. [7] proposed a hybrid method integrating statistical features (e.g., keywords' TF-IDF weights, term frequency, sentence position, word length, weighted parts-of-speech (POS) tags) with linguistic features (e.g., proper nouns, pronouns), employing a successive threshold to optimize high-weight sentence extraction. Later, inspired by the manual document drafting process, where core topics are first identified and then expanded with relevant words, researchers developed topic model-based extractive summarization methods. These approaches map documents into a semantic space, uncover latent semantic relationships (e.g., topics) between documents and words through term frequency analysis, and select sentences that optimally represent these topics  
Figure 1: Extraction–abstraction two-stage framework. “-” represents a Chinese character in a sentence. as summaries [8]. [PERSON] and [PERSON] [9] proposed an extractive summarization method using the latent semantic analysis (LSA) topic model to identify semantically salient sentences. The method applies singular value decomposition (SVD) to the sentence-term matrix, producing singular vectors and corresponding index values. Each singular vector corresponds to a latent document topic, with the index value representing the optimal sentence score in that topic dimension. Sentences with the largest index values from those possessing the most significant singular vectors are selected to generate the summary. [PERSON] et al. [10] proposed a method for automatically summarizing changes in dynamic text collections. Their approach uses the latent Dirichlet allocation (LDA) topic model to identify latent topic structures of changes, computes term-level and sentence-level latent topic scores, and selects sentences with maximum topic relevance for summary generation. Inspired by Google's PageRank algorithm for web page ranking [11], researchers proposed graph-based extractive summarization methods. These approaches model sentences as nodes and their relationships as edges in a graph, rank them iteratively based on connectivity, and select top-ranked sentences (basically centroids in the graph) from source documents to form summaries. These methods posit that a sentence's importance is determined by its interrelationships with others in the document (or document collection). The more a sentence is supported by others, the higher its importance [12]. TextRank [13] and LexRank [14] are two classic graph-based extractive summarization algorithms that differ primarily in their sentence similarity computation mechanisms. TextRank measures similarity through word overlap (i.e., the count of co-occurring words between sentences), while LexRank uses TF-IDF-weighted cosine similarity.