# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 5 Experiments
### Ablation Study  
To rigorously evaluate the contributions of integrating the group transformer mechanism into the hypergraph neural network within the HGTMFS framework, we conducted a comprehensive series of ablation experiments utilizing diverse structural variants. Specifically, we designed three distinct ablation models to systematically and quantitatively assess the impacts of the key components of our proposed method. These experiments were also \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Daily Mail Dataset**} \\ \hline Model & Rouge-1 & Rouge-2 & Rouge-L & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 \\ \hline S2S [47] & 20.42 & 1.63 & 15.12 & 12.35 & 8.14 & 5.67 & 3.89 \\ PG [46] & 28.41 & 3.46 & 37.97 & 19.52 & 13.45 & 9.01 & 6.03 \\ TextRank [48] & 24.20 & 9.11 & 22.14 & 14.78 & 10.03 & 6.89 & 4.76 \\ Lead-3 & 35.81 & 15.34 & 26.13 & 22.67 & 16.45 & 11.23 & 7.89 \\ MSMO [44] & 35.38 & 14.79 & 25.41 & 23.15 & 17.02 & 12.34 & 8.76 \\ M2 SM [43] & 41.73 & 18.59 & 31.68 & 27.84 & 20.56 & 15.33 & 10.89 \\
**HGTMFS (ours)** & **45.00** & **21.00** & **35.50** & **30.50** & **23.40** & **17.80** & **13.20** \\ \hline \hline \multicolumn{10}{c}{**CNN Dataset**} \\ \hline Model & Rouge-1 & Rouge-2 & Rouge-L & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 \\ \hline S2S [47] & 26.88 & 1.60 & 18.82 & 15.47 & 10.22 & 6.78 & 4.12 \\ PG [46] & 26.43 & 1.50 & 17.24 & 17.88 & 11.30 & 7.24 & 4.89 \\ TextRank [48] & 21.95 & 7.12 & 19.25 & 13.64 & 9.21 & 6.12 & 4.01 \\ Lead-3 & 11.70 & 3.40 & 13.94 & 9.56 & 6.47 & 4.28 & 2.97 \\ MSMO [44] & 26.83 & 8.11 & 18.34 & 18.92 & 12.87 & 9.10 & 6.25 \\ M2 SM [43] & 27.81 & 8.87 & 18.73 & 19.76 & 13.54 & 9.67 & 6.87 \\
**HGTMFS (ours)** & **31.00** & **11.50** & **22.00** & **22.15** & **15.98** & **11.45** & **8.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different summarization models on the Daily Mail and CNN datasets. conducted on two prominent benchmark datasets: the Daily Mail dataset and the CNN dataset. By comparing the results across these two datasets, we aimed to provide a nuanced understanding of the performance and generalizability of our method, ensuring a robust evaluation of its efficacy in different contexts. * **Mean Aggregation** computes node representations by averaging the features of neighboring nodes, offering a simple and efficient approach in graph neural networks. While it reduces computational complexity, this method may overlook complex node relationships, potentially limiting performance in tasks requiring nuanced interactions. Despite its limitations, mean aggregation serves as a fundamental baseline for comparing more advanced techniques. * **Full self-attention** dynamically weighs all node interactions using attention scores, enabling the capture of intricate dependencies within the graph. This mechanism enhances representational power but comes with high computational costs due to its quadratic complexity. The balance between performance gains and computational burden is a critical factor when employing Full Self-attention in graph models. * **Homogeneous attention** assigns equal attention weights to all node pairs, simplifying computation and reducing complexity compared to Full Self-attention. This uniform approach can be advantageous in uniform graph structures but may fail to capture subtle relationships, impacting performance on tasks requiring detailed interaction modeling. Homogeneous attention provides a middle ground in the spectrum of aggregation mechanisms. The ablation study presented in Table 3 provides a comprehensive comparison of different methods on the ROUGE and BLEU metrics for both the Daily Mail and CNN datasets. The results unequivocally demonstrate that our proposed HGTMFS method consistently outperforms the other three mechanisms--mean aggregation, full self-attention, and homogeneous attention--across both datasets. For the Daily Mail dataset, HGTMFS achieves the best performance across all metrics, including ROUGE-1, ROUGE-2, ROUGE-L, and BLEU-1 to BLEU-4.