## 5 Experiments and Results
### Experimental Results  
The experimental results demonstrate the effectiveness of Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES) in the task of automatic extractive summarization. As shown in Table 3, MCHES achieves the highest ROUGE scores across all three metrics (R-1, R-2, and R-L) when evaluated on the CNN/DailyMail dataset, outperforming all baselines and variant models. The full MCHES model surpasses the strongest baseline, GenCompareSum, by 3.662% in R-1, 3.395% in R-2, and 2.166% in R-L. This significant improvement highlights the synergistic effect of the multi-element hypergraph construction along with the contextual homogenization and attention mechanisms implemented within MCHES. Further analysis of the ablation study reveals the importance of each component. The removal of the Contextual Homogenization Module (MCHES-NoCHM) results in a performance drop, suggesting that the integration of heterogeneous hyperedge information plays a substantial role in capturing the document context. The performance decline in the MCHES-NoHCA and MCHES-NoReadout variants further emphasizes the contributions of the Hypergraph Contextual Attention Module and the Extractive Read-out Strategy, respectively. Notably, the MCHES-BasicGraph variant, which employs a simplified graph structure, demonstrates the added value of the multi-element contextual representation, as its removal leads to a noticeable decrease in ROUGE scores. These results corroborate the hypothesis that a comprehensive representation of document structure and context, combined with focused attention and strategic read-out, is critical to producing high-quality summaries. The empirical evidence suggests that the MCHES framework can effectively leverage the intricate relationships within texts, significantly enhancing the summarization output. The evaluation of Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES) on the Xsum dataset, as presented in Table 4, has yielded insightful findings. The proposed MCHES model outperforms all baseline models and ablated versions across the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **R-1** & **R-2** & **R-L** \\ \hline LEAD-3 & 39.447 & 20.086 & 38.208 \\ \hline SummaRuNser & 34.862 & 17.258 & 33.516 \\ \hline NeuSum & 37.573 & 18.106 & 33.968 \\ \hline BanditSum & 37.898 & 18.287 & 33.986 \\ \hline JECS & 37.956 & 19.051 & 35.499 \\ \hline HIBERT & 38.378 & 20.029 & 35.806 \\ \hline BERTSUMEXT & 39.638 & 20.161 & 38.269 \\ \hline NeRoBERTa & 40.228 & 21.101 & 38.836 \\ \hline MatchSum & 40.584 & 21.512 & 39.857 \\ \hline HAHSum & 40.929 & 21.546 & 40.043 \\ \hline GenCompareSum & 41.094 & 21.568 & 40.311 \\ \hline MCHES (proposed model) & **44.756** & **24.963** & **42.477** \\ \hline MCHES-NoCHM & 43.847 & 23.491 & 41.601 \\ \hline MCHES-NoHCA & 42.236 & 21.629 & 40.616 \\ \hline MCHES-NoReadout & 42.709 & 22.295 & 41.271 \\ \hline MCHES-BasicGraph & 43.599 & 23.394 & 41.376 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ROUGE scores for the CNN/DailyMail dataset. ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L) metrics, reinforcing the efficacy of its comprehensive design. MCHES's superiority is pronounced in its ROUGE-2 score, which, at 8150, is significantly higher than the next-best model, GenCompareSum, at 7552. This suggests that MCHES's sophisticated approach to understanding and leveraging the nuanced relationships between sentences and document structure is particularly effective. The large margin in ROUGE-2, which measures bigram overlap, indicates a robust capacity for capturing and reproducing the most salient content, a critical aspect of effective summarization. The ROUGE-L score, reflecting the longest common subsequence, also sees MCHES leading by a substantial margin. With a score of 17,318, MCHES demonstrates a strong ability to maintain sentence structure and coherence, which is essential to producing readable and fluent summaries. The ablation study results shed light on the contribution of individual MCHES components.