## 2 Literature Review  
### Multi-Document Summarization  
[PERSON] et al. [10] provided a theoretical basis and practical framework for designing and optimizing MDS models. Depending on the summary implementation method, MDS can be divided into extractive and abstractive methods. Earlier extractive MDS methods include LexRank [11], proposed by [PERSON] et al., and TextRank [12], proposed by [PERSON] et al. Both of them are unsupervised methods and graph-based scoring models. The centroid-based method [13] is a typical traditional MDS method. It scores sentences separately according to features such as centrality, position, and term frequency-inverse document frequency (TF-IDF) and then combines all scores linearly into an overall sentence score. [PERSON] et al. [14] proposed a topic-based general multi-document summary sentence sorting method using the latent Dirichlet allocation (LDA) model to calculate the weight of a sentence and the similarity between the sentence topics. Then, the information is integrated into a two-dimensional coordinate system, and the sentence sorting problem is solved as a maximum vector problem. In addition, there are methods based on TF-IDF [15], clustering-based methods [16; 17], and methods based on latent semantic analysis (LSA) [18; 19]. Most of the above extractive methods adopt the idea of single-document summary processing but overlook the hierarchical relationship between documents, and this relationship plays a crucial role in multi-document summary tasks [20]. As a result, many researchers have turned to more advanced abstractive summarization methods that can understand and integrate the relationships between documents. [PERSON] et al. [21] used a sentence fusion method to identify fragments that convey standard information across documents and combine them into sentences. [PERSON] et al. [22] employed the sequence-to-sequence model, using a long short-term memory (LSTM) network as the encoder structure to encode words and sentences in the original document and then using the LSTM-based hierarchical decoder structure to generate the summary. [PERSON] et al. [23] proposed a hierarchical transformer architecture to encode multiple documents hierarchically and represent potential cross-document relationships through an attention mechanism that shares information between multiple documents. [PERSON] et al. [9] introduced explicit graph representation in the encoder and decoder of the hierarchical transformer architecture and used the cosine similarity between paragraphs to obtain potential connections between documents better. [PERSON] et al. [24] combined dependency analysis with the transformer architecture, introduced an external parser into the encoding part to capture the dependency tree, and combined the dependency parsing information and source documents through a language-guided multi-head attention mechanism to generate semantic-rich features. However, capturing cross-document relationships based on word or paragraph representation is not straightforward and flexible, so many researchers have begun to model multiple documents more effectively by mining subtopics. [PERSON] et al. [25] used topic analysis technology to discover explicit or implicit subtopics related to a given topic and then ranked sentences by fusing multiple modalities built on different subtopics through a multimodal learning algorithm. [PERSON] et al. [26] took advantage of the topic model and supervised features to generate multi-document summaries by integrating the information of the sentences into the Bayesian topic model. [PERSON] et al. [27] extracted relevant topic words from source documents through topic models and used them as elements of fuzzy sets. Meanwhile, they generated fuzzy relevance rules through sentences in the source documents and, finally, generated multi-document summaries by the fuzzy inference system. [PERSON] et al.