## 5 Experiments and Results
### Experimental Results
The decline in performance observed in MCHES-NoCHM suggests that the Contextual Homogenization Module plays a vital role in summarization by effectively integrating diverse contextual information from multiple types of hyperedges. The MCHES-NoHCA model, lacking the Hypergraph Contextual Attention Module, shows a lower but still significant drop in ROUGE scores. This underscores the importance of the dual-level attention mechanism in filtering and focusing on the most informative elements of the hypergraph. Notably, MCHES-NoReadout exhibits a decrease in performance, affirming the significance of the Extractive Read-out Strategy in the final summary construction. The attention-based pooling mechanism employed in the read-out phase contributes markedly to the selection of sentences that best represent the main points of a document. MCHES-BasicGraph, which employs a simpler graph structure without the multi-element enhancements, provides a baseline to understand the additional value brought by the richer multi-element contextual hypergraph representation. The empirical results from our experiments on the Xsum dataset demonstrate the robustness of the MCHES framework, with particular strengths in understanding and reproducing complex document \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **R-1** & **R-2** & **R-L** \\ \hline LEAD-3 & 25,512 & 7355 & 16,069 \\ \hline SummaRuNNer & 23,194 & 5626 & 14,867 \\ \hline NeuSum & 24,194 & 5699 & 15,056 \\ \hline BanditSum & 24,577 & 6416 & 15,150 \\ \hline JECS & 25,005 & 6709 & 15,332 \\ \hline HIBERT & 25,145 & 6728 & 15,582 \\ \hline BERTSUMEXT & 25,145 & 6747 & 15,631 \\ \hline NeRoBERTa & 25,225 & 7011 & 15,723 \\ \hline MatchSum & 25,431 & 7022 & 15,893 \\ \hline HAHSum & 25,450 & 7239 & 15,901 \\ \hline GenCompareSum & 25,593 & 7552 & 16,199 \\ \hline MCHES (proposed model) & **26,422** & **8150** & **17,318** \\ \hline MCHES-NoCHM & 25,775 & 7700 & 16,498 \\ \hline MCHES-NoHCA & 26,059 & 7771 & 16,758 \\ \hline MCHES-NoReadout & 26,174 & 7938 & 17,311 \\ \hline MCHES-BasicGraph & 25,605 & 7576 & 16,260 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ROUGE scores for the Xsum dataset. content. The outcomes of the ablation study confirm the necessity of each module within MCHES, offering valuable insights into their functional roles. Overall, MCHES sets a new standard for extractive summarization, particularly in contexts demanding high levels of semantic comprehension and structural awareness. The empirical results presented in Table 5 for the PubMed dataset indicate a clear advantage of the MCHES framework over traditional and contemporary baseline models. The proposed model, MCHES, exhibits superior performance, with the highest ROUGE scores, suggesting its effectiveness in summarizing scientific papers, which are characterized by complex structures and specialized vocabulary. The MCHES model demonstrates a remarkable improvement in R-2 scores, achieving 19,129, which is a significant leap from the closest baseline, GenCompareSum, with a score of 15,345. This performance is indicative of MCHES's ability to identify and retain key information from highly technical and content-dense documents. Moreover, the high R-L score achieved by MCHES suggests that it is particularly adept at maintaining sentence structure and coherence, essential to summarizing scientific articles that require the precise replication of the original content's complexity. The results tabulated in Table 6 showcase the performance of various summarization models on the CNN/DailyMail, Xsum, and PubMed datasets by using BERTScore, a metric that reflects semantic similarity with human references. The MCHES framework, our proposed model, attains the highest BERTScore values across all three datasets, indicating its superior ability to produce semantically relevant summaries.