## 5 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **R-1** & **R-2** & **R-L** \\ \hline LEAD-3 & 39,554 & 12,822 & 33,352 \\ \hline SummaRuNNer & 37,949 & 12,759 & 32,537 \\ \hline NeuSum & 40,496 & 13,909 & 33,353 \\ \hline BanditSum & 40,601 & 13,987 & 33,983 \\ \hline JECS & 41,120 & 14,095 & 34,129 \\ \hline HIBERT & 41,152 & 14,410 & 34,186 \\ \hline BERTSUMEXT & 41,161 & 14,964 & 34,207 \\ \hline NeRoBERTa & 41,234 & 15,049 & 34,881 \\ \hline MatchSum & 41,527 & 15,214 & 34,955 \\ \hline HAHSum & 41,672 & 15,270 & 34,963 \\ \hline GenCompareSum & 41,892 & 15,345 & 35,222 \\ \hline MCHES (proposed model) & **44,321** & **19,129** & **37,505** \\ \hline MCHES-NoCHM & 41,938 & 15,710 & 35,415 \\ \hline MCHES-NoHCA & 42,148 & 16,605 & 35,683 \\ \hline MCHES-NoReadout & 42,294 & 16,842 & 36,137 \\ \hline MCHES-BasicGraph & 42,533 & 17,873 & 36,968 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ROUGE scores for the PubMed dataset. The proposed MCHES model demonstrates exceptional semantic fidelity, as evidenced by its leading BERTScore values of 59,995 on CNN/DailyMail, 88,424 on Xsum, and 89,285 on PubMed. This underscores the model's effectiveness in generating summaries that are not only topically accurate but also semantically aligned with the reference summaries. The ablation study results incorporated into the BERTScore evaluation further elucidate the significance of each MCHES component. MCHES-NoCHM exhibits a decrease in scores across all datasets when compared with the full MCHES model, affirming the contribution of the Contextual Homogenization Module to semantic coherence. The performance dip of MCHES-NoHCA emphasizes the value added by the Hypergraph Contextual Attention Module in enhancing semantic alignment with reference summaries. Interestingly, MCHES-NoReadout and MCHES-BasicGraph maintain relatively high BERTScore values, suggesting that while the full MCHES model is the most effective, its individual components still positively contribute to semantic performance. MCHES-BasicGraph, in particular, scores higher than the complete MCHES model on the Xsum and PubMed datasets, indicating that a simplified graph structure may sometimes yield summaries with slightly more semantic similarity to references, possibly due to less complexity in the model leading to reduced overfitting or noise. The performance of the summarization models, as presented in Table 7 using MoverScore, aligns with the semantic coherence and content relevance evaluations observed in prior assessments. MCHES stands out across the board, substantiating its effectiveness in generating summaries that closely resemble human references in terms of content movement and ordering. The MCHES model secures the highest MoverScore across all datasets, with particularly notable margins on the PubMed dataset, suggesting that its approach is exceptionally well suited for structurally and semantically complex texts. This aligns with the model's design, which intricately processes and integrates multi-dimensional content representations, reaffirming the importance of the MCHES framework's comprehensive architectural components for summarization. Interestingly, the ablation study reveals a minimal decline in MoverScore for models lacking individual MCHES components (MCHES-NoCHM, MCHES-NoHCA, and MCHES-NoReadout), indicating that while each