# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 2 Related Work  
Our research builds upon previous work in the fields of multimodal summarization and hypergraph neural networks.  
**Text Summarization.** Automatic text summarization involves creating a succinct and coherent summary that retains the essential content and meaning of the original text [12]. A variety of methodologies have been proposed to tackle this task [13; 14; 15; 16; 17; 18]. For instance, [PERSON] et al. [16] introduced BART, a denoising autoencoder designed to pretrain sequence-to-sequence models that demonstrates strong performance when fine-tuned for text generation tasks. [PERSON] et al. [18] presented a novel domain feature miner by reframing the feature mining challenge as a clustering problem, incorporating three new empirical findings to enhance text summarization. [PERSON] et al. [14] developed sentence topic embeddings and sentence content embeddings to capture long-range semantic relationships and structural content in texts. [PERSON] et al. [13] introduced the DEPSYM++ simplifier, designed to perform targeted sentence simplifications to address appositive clauses, relative clauses, conjoined clauses, and passive voice constructions. Each of these approaches strives to maximize the utility of textual data for generating effective summaries.  
**Multimodal Summarization.** Multimodal summarization focuses on distilling the most significant aspects of input data that span multiple modalities. Recent years have seen extensive exploration into generating text summaries from multimodal data, which may include text, visual, and auditory information [19; 20; 21]. A significant body of work has concentrated on incorporating visual information to enhance the quality of text summarization [3; 4; 5; 22; 23]. [PERSON] and [PERSON] [3] proposed a multimodal attention mechanism to generate summaries by integrating visual and textual information. [PERSON] et al. [4] introduced a multimodal sentence summarization task, which generates a concise text summary from an extended sentence and its corresponding image. Additionally, [PERSON] et al. [5] developed a multimodal selective gate network to guide the encoder towards focusing on critical information within the input text.  
**Multimodal Fusion.** Multimodal fusion integrates diverse sensory inputs to enhance machine learning models, yet traditional methods like early and late fusion often struggle with modality heterogeneity and information loss [24; 25]. Recent techniques, such as dynamic multimodal fusion (DynMM) [26] and progressive fusion [24], offer adaptive and iterative processes to improve expressiveness and efficiency. Trustworthy fusion strategies like multimodal dynamics focus on assessing informativeness to enhance reliability, especially in critical domains [27]. Graph-based approaches, including HyperRep [28], use high-order correlations for richer representations, while BalanceMA addresses modality imbalances adaptively [24]. Orthogonal sequential fusion (OSF) further improves integration by selectively weighting modalities to optimize complementary information extraction [29].  
**Hypergraph Learning.** Hypergraph networks, which link multiple nodes via a single hyperedge, are effective for capturing intricate relationships. This methodology has been leveraged across various domains, including clustering [30; 31], classification [32; 33], traffic forecasting [34], representation learning [28], and recommendation systems [35; 36; 37]. To process data structured as hypergraphs, hypergraph convolutional networks (HGCNs) extend traditional GCNs [38; 39].