# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 1 Introduction  
As multimodal learning continues to advance, the field of multimodal summarization has garnered significant attention. Unlike traditional unimodal summarization tasks--such as summarizing images or text independently--multimodal summarization seeks to generate cohesive summaries by integrating information from multiple modalities. Given the rapid expansion of online content, including news, livestreams, vlogs, and more, multimodal summarization presents substantial potential for real-world applications [1; 2]. The core objective of multimodal summarization is to distill the most pertinent information from diverse modalities into unified summaries. While current models have achieved promising outcomes, their focus has largely been on capturing superficial correlations between modalities [3; 4; 5]. These models typically encode each modality separately into distributed representations, which are then combined through loosely integrated frameworks [6; 7]. Such an approach tends to overlook the nuanced semantic interactions that exist in fine-grained multimodal signals. For effective multimodal summarization, it is crucial to account for complex cross-domain interactions--such as token-to-token, token-to-patch, and patch-to-patch relationships within multimodal data--and the semantic connections between textual entities and visual objects. Failure to incorporate these fine-grained interactions can lead to significant limitations: (1) an insufficient integration of information across modalities, often resulting in an over-reliance on one modality while neglecting others, and (2) an inability to model higher-order relationships that are essential for comprehensive multimodal summarization, particularly those involving both intra-modal and inter-modal information exchanges. Capturing these complex, fine-grained cross-modal interactions to facilitate multimodal summarization within a single framework is a challenging endeavor. A prevalent strategy involves representing the informative elements of multimodal data as nodes within a graph, with their interactions depicted as edges [8; 9]. Graph neural networks (GNNs) are then utilized to merge this cross-domain information. However, traditional GNNs often fall short in capturing higher-order features, as they primarily concentrate on node aggregation and may overlook the rich information contained within the connecting edges. Furthermore, GNNs typically employ a one-to-one mapping mechanism, which complicates the modeling of intricate data correlations that require subtle information integration, such as the relationships between textual phrases or between patches and objects within images. In such contexts, connecting distant nodes often necessitates multiple hops, relying on multi-layer aggregation within GNNs. Unfortunately, challenges such as oversmoothing and the introduction of noise--particularly in deep GNN architectures--can significantly diminish the model's overall efficacy [10; 11]. To address these challenges, we introduce a novel framework specifically designed for multimodal summarization, referred to as hypergraph transformer-based multimodal summarization (HGTMPS). This framework is specifically designed to capture high-order relationships across different modalities. Unlike conventional approaches, HGTMPS simultaneously models the complex interactions between various modalities using hypergraphs. This framework extracts features from both textual and visual elements to construct a hypergraph that integrates both text and image nodes. By leveraging the transformer mechanism to propagate information within the hypergraph, HGTMS facilitates efficient information exchange, thereby enhancing the integration of high-order data across multimodal entities. Experimental results over several benchmark datasets demonstrate the superiority of our proposal.