## 6 Discussion  
In this section, we delve into the insights gleaned from our experimental evaluations, as delineated by the evaluation metrics. Our discussion interprets the significance of the empirical results for the MCHES model variants and compares their performance with established baseline models as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Algorithm** & **R-1** & **R-2** & **R-L** & **BERTScore** \\ \hline \multirow{3}{*}{CNN/DailyMail} & TF-IDF & 30.450 & 15.210 & 28.370 & 50.120 \\ \cline{2-6}  & MCHES (proposed) & **44.756** & **24.963** & **42.477** & **59.995** \\ \hline \multirow{3}{*}{XSum} & TF-IDF & 23.320 & 6.540 & 14.890 & 48.750 \\ \cline{2-6}  & MCHES (proposed) & **26.422** & **8.150** & **17.318** & **88.424** \\ \hline \multirow{3}{*}{PubMed} & TF-IDF & 32.140 & 16.890 & 30.120 & 52.450 \\ \cline{2-6}  & MCHES (proposed) & **44.321** & **19.129** & **37.505** & **89.285** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison between TF-IDF and MCHES framework.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **HAHSum** & **GenCompareSum** & **MCHES** \\ \hline Coherence & 3.5 & 3.8 & 4.2 \\ \hline Readability & 3.7 & 4.0 & 4.3 \\ \hline Informativeness & 3.4 & 3.6 & 4.1 \\ \hline Relevance & 3.6 & 3.7 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Manual evaluation results.  
* **Outstanding performance of MCHES**:
* MCHES outperforms the baseline models on all datasets, indicating its effective capture of semantic content and structure.
* The substantial lead in MoverScore on the PubMed dataset suggests pronounced strength in processing technical and domain-specific texts.
* **Significance of MCHES components**:
* The minimal performance drop for the MCHES-NoCHM and MCHES-NoHCA variants suggests a strong base model that is enhanced, rather than defined, by its individual components.
* The MCHES-NoReadout variant, despite the absence of the read-out strategy, maintains competitive scores, underlining the robustness of the attention mechanisms within the model.
* **Performance consistency across datasets**:
* MCHES exhibits consistent performance across different datasets, reinforcing the model's adaptability and generalizability.
* The consistently high performance of MCHES-BasicGraph, particularly on the Xsum dataset, challenges the assumption that model complexity is a prerequisite for higher semantic alignment.
* **Implications for future research:*
* The results prompt a re-evaluation of component contributions in complex models, suggesting potential areas for simplification without significant performance losses.
* Future research might explore the trade-off between model complexity and performance, especially in the context of domain-specific summarization tasks.  
The empirical evidence presented firmly positions the MCHES framework as a leading approach to extractive summarization, capable of understanding and replicating the intricate semantic relationships in texts. The findings from our ablation study contribute to a nuanced understanding of the role each architectural element plays, setting the stage for subsequent advancements in the field of NLP.