# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 2 Related Work
### Abstractive Summarization  
Abstractive summarization fundamentally differs from extractive methods. Unlike extractive approaches that select existing sentences, abstractive methods aim to semantically comprehend document content and generate novel expressions that concisely encapsulate core ideas, thereby producing human-readable summaries [29]. To achieve this, they employ advanced natural language generation (NLG) techniques such as sentence compression, sentence fusion, concept fusion and generalization, and lexical paraphrasing [30; 31]. [PERSON] et al. [32] developed a domain-specific template-based approach for abstractive summarization of Kannada language documents. They used abstraction schemes and information extraction rules to identify the relevant content, populated predefinedtemplates with the extracted data, and generated coherent sentences from these templates to produce the final summaries. [PERSON] et al. [33] proposed a tree-based abstractive multi-document summarization approach. Their method involves parsing input text into a collection of syntactic dependency trees, employing a maximum density sub-graph cutting algorithm to select variable-sized partial dependency trees representing salient knowledge granules, and utilizing a transition-based syntactic linearization model to combine these granules into coherent summary sentences. [PERSON] et al. [34] proposed a semantic graph-based abstractive multi-document summarization method. They first constructed a semantic graph with nodes defined as predicate argument structures (PASs) representing sentence-level semantics, and edges weighted by semantic similarity between PASs. Node relationships were augmented through PAS-document and PAS-document set relationships. A hybrid ranking strategy combining a graph-based ranking algorithm (e.g., PageRank) and the MMR algorithm prioritized salient PASs while reducing redundancy. Finally, NLG techniques transformed the top-ranked PASs into fluent summary sentences. With the rapid advancement of computational capabilities, particularly through the widespread adoption of high-performance computing devices such as GPUs and TPUs, deep learning-based approaches for automatic sentence-level summarization have achieved substantial progress. Among these, the Seq2 Seq (sequence-to-sequence) model, proposed by [PERSON] et al. [35] and [PERSON] et al. [36] for machine translation, catalyzed breakthroughs in abstractive text summarization techniques. The Seq2 Seq model employs an encoder-decoder neural architecture: the encoder compresses the source text into a fixed-length context vector, which the decoder then iteratively decodes to generate the target output sequence. [PERSON] et al. [37] were the first to introduce the Seq2 Seq model to abstractive summarization tasks in 2015 and proposed an attention-based summarization (ABS) model. ABS employed an attention-based encoder to generate context-aware semantic embeddings, paired with a feed-forward neural decoder using beam search for sentence generation. Building on this foundation, [PERSON] et al. [38] introduced the recurrent attentive summarizer (RAS) in 2016, which replaced the feed-forward decoder with an attentive recurrent neural network (RNN) and augmented the encoder with convolutional layers to capture local context dependencies, thereby achieving significant performance improvements. [PERSON] et al. [39] proposed an RNN-based encoder-decoder architecture. The encoder incorporated linguistic features such as POS tags, named entity tags, and discretized TF and IDF values to enhance keyword recognition. For decoding, they introduced a switching generator-pointer model to handle OOV words. Additionally, a hierarchical attention mechanism was implemented to model document structure through word-level and sentence-level context interactions, thus improving document representation. [PERSON] et al. [40] proposed a hierarchical long short-term memory (LSTM)-based encoder-decoder framework for abstractive summarization.