## 6 Discussion  
This study introduces an MIS-based graph filtering approach, integrated with LLMs, to enhance summarization performance. The proposed framework shows clear improvements in accuracy, semantic coherence, and computational efficiency, especially for 200-word summaries. Significant improvements were observed when using models such as XLNet, RoBERTa, DistilBERT, ALBERT, and Pegasus. However, ELECTRA performed relatively poorly, likely due to its masking-based strategy, which may not align well with the framework's independent node selection. This highlights the need for tailored sentence selection strategies for models like ELECTRA.  
Analyses of 400-word summaries show that the framework remains effective with longer content. The results confirm that it improves LLMs' summarization and supports strong word-level representations. The notable gains with ALBERT likely stem from its ability to maintain semantic coherence. The summaries are structurally aligned with the original texts and preserve long-range contextual links. This suggests that even smaller LLMs can benefit from the framework. Selecting more information-dense sentences also enhances computational efficiency. By removing out-of-context content, the method produces clearer and more coherent summaries. In 200-word summaries, a consistency in word overlap, order, and sentence structure indicates the successful retention of key textual features. The use of MIS theory further supports contextual preservation by filtering irrelevant information.  
Evaluations of three-word summaries showed a stronger integration with models like XLNet and ALBERT, leading to a better contextual coherence and handling of long sentence structures. Sentence selection was more effective with these models. The MIS-based structure reduced computational loads and enabled shorter, yet meaningful and dense, summaries. Pegasus, however, underperformed in short summaries, possibly due to its large architecture being less suited to structural modifications in brief contexts. In contrast, its performance improved significantly in 200- and 400-word summaries, suggesting better alignment with long-text summarization tasks.  
Overall, the MIS-based approach has improved sentence selection in LLMs, leading to more realistic summaries. Strong results with models like ALBERT, despite its low parameter count, highlight the framework's versatility. The MIS-driven structural transformation reduced computational demands, allowing a faster and resource-efficient generation of content-rich summaries.  
Integrating structural and conceptual information has enabled more reliable inferences and clearer cause-effect relationships within the text. The graph-based structure preserved contextual links, reducing the risk of information loss. In conclusion, the experimental results show that combining MIS-based graph algorithms with LLMs creates an effective synergy in summarization, improving both accuracy and efficiency. The proposed framework offers valuable contributions to the field, both in theory and application.  
By extending the capabilities of LLMs, the framework also improves their accessibility and practical utility.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Rouge Metrics** & **t-Statistic** & \(p\)**-Value** \\ \hline  & Rouge-1 & 10.156 & 0.0005 \\  & Rouge-2 & 8.500 & 0.0011 \\  & Rouge-L & 12.509 & 0.0002 \\  & Rouge-W & 10.954 & 0.0004 \\  & Rouge-S & 9.436 & 0.0007 \\  & Rouge-SU & 15.922 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._