# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 2 Related Work
### Abstractive Summarization
The encoder processes words through a word-level LSTM, followed by a sentence-level LSTM to encode sentences of a document. To identify salience content, they introduced a graph-based attention mechanism inspired by PageRank, which identifies key sentences through graph centrality scoring. During decoding, a distraction mechanism [41] is applied to penalize redundant content generation. Additionally, a hierarchical decoder with a reference mechanism is designed to improve summary fluency and factual accuracy. The Google research team proposed PEGASUS [42], a pre-trained model based on the Transformer encoder-decoder architecture, specifically designed for abstractive text summarization. This model aims to enhance the ability to capture key information and generate fluent, human-like summaries. [PERSON] et al. [43] developed an imitation learning-based knowledge distillation framework to mitigate exposure bias in sequence generation tasks. Text representation is a critical step in automatic text summarization, as the quality of representation directly determines the accuracy and completeness of generated summaries. Traditional summarization methods typically rely on bag of words (BoW) frameworks--such as VSM and TF-IDF weighting schemes--to convert textual content into numerical vectors. While these BoW-based approaches can capture statistical lexical patterns, they inherently fail to model word order and contextual dependencies. This limitation leads to the loss of essential syntactic structures and semantic nuances present in the source document, thereby constraining the model's ability to produce summaries with contextual coherence and semantic precision. The recent rapid advancement of deep learning technologies, particularly the emergence of pre-trained language models (PLMs), has revolutionized text representation and generation. PLMs are artificial intelligent models that learn deep linguistic features and contextualized representations through self-supervised pre-training on large-scale text corpora. Notable implementations such as GPT (generative pre-training) [44] and BERT (Bidirectional Encoder Representations from Transformers) [45] have demonstrated state-of-the-art performance across NLP tasks, including automatic text summarization. By learning generalizable language patterns, these models generate semantically coherent and context-aware outputs that significantly outperform traditional methods. In 2019, [PERSON] [46] proposed BERTSUM, an extractive summarization model that builds on the BERT architecture to enable more precise sentence extraction. Subsequently, [PERSON] and [PERSON] [47] further developed a unified framework integrating abstractive methods, significantly advancing the generalization capabilities of summarization techniques. In the same year, [PERSON] et al. [48] introduced GPT-2, an enhanced version of GPT trained on large-scale text corpora with expanded parameters. The model demonstrated unprecedented linguistic generation capabilities and context-aware modeling, achieving state-of-the-art performance in open-domain text generation tasks. In 2020, [PERSON] et al. [49] employed the GPT-2 model to generate abstractive summaries for COVID-19-related medical research articles. By producing summaries with enhanced readability and sufficient information coverage, this work provided strong support for the World Health Organization (WHO) and medical communities in developing treatments and measures against COVID-19. This study demonstrated that deep learning-based abstractive summarization approaches are not only theoretically advanced, but also exhibit substantial potential in real-world applications.