## 5 Experiment Setup and Results
### Experimental Results
 } & R & 0.15739 & **0.183004** & 0.14651 & **0.189503A** & 0.19473 & **0.19458A** & 0.12382 & **0.16971A** & 0.16493 & **0.23057A** & 0.19069 & **0.23903A** \\  & & (+0.0229) & (+0.0401) & (+0.0010) & (+0.0051) & (+0.0058) & (+0.0071) & (+0.007 \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**ROUGE Performance Values (DUC—200-Word Abstracts)**} \\ \cline{2-13}  & & \multicolumn{2}{c}{**XLNet**} & \multicolumn{2}{c}{**Roberta**} & \multicolumn{2}{c}{**DistDistler**} & \multicolumn{2}{c}{**Electra**} & \multicolumn{2}{c}{**Albert**} & \multicolumn{2}{c}{**Regauss**} \\  & \multicolumn{2}{c}{**Baseline**} & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** \\ \hline \multirow{9}{*}{
\begin{tabular}{} \end{tabular} } & R & 0.21556 & **0.22310A** & 0.18611 & **0.22934A** & 0.22315 & **0.22958A** & 0.18717 & **0.21921A** & 0.20501 & **0.26325A** & 0.23038 & **0.25369A** \\  & & (4,0.0105) & & (0.0432) & (-0.0067) & (-0.0432) & (-0.0320) & (+0.0582) & (+0.0231) \\  & P & 0.21001 & **0.22102A** & 0.18547 & **0.21856A** & 0.22017 & **0.22460A** & 0.18474 & **0.21800A** & 0.20598 & **0.25599A** & 0.22319 & **0.22334** \\  & & (+0.1119) & & (0.0330) & (+0.0044) & (+0.0032) & (+0.0332) & (+0.0500) & (+0.02914) & (+0.02914) \\  & F1 & 0.21259 & **0.22188A** & 0.18500 & **0.22359A** & 0.22128 & **0.22675A** & 0.18546 & **0.21853A** & 0.20541 & **0.25914A** & 0.22687 & **0.22563A** \\  & & (+0.0037) & & (0.0380) & (+0.0054) & (+0.0030) & (+0.0030) & (+0.00537) & (+0.0057) & (+0.02606) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
The ROUGE-2 metric measures the contextual accuracy of summaries over two-word sequences. The proposed model demonstrates notable improvements in F1 scores, with enhancements observed in XLNet (+0.0406), ALBERT (+0.0812), and DistilBERT (+0.0147), as shown in Table 5. The ROUGE-L metric, which evaluates the preservation of long sentence structures, revealed positive impacts on F1 scores for XLNet (+0.0557) and ALBERT (+0.0303). However, no improvements were observed in the other LLMs. RoBERTa was disadvantaged in selections lacking contextual awareness. However, improvements in the ROUGE-W metric were observed in the F1 scores of the proposed model, including XLNet (+0.0341) and ALBERT (+0.0196). Notably, the substantial improvement observed in the XLNet model indicates the effective integration of the proposed approach. In the ROUGE-S and ROUGE-SU metrics, which assess contextual structure and sentence sequencing, the proposed model shows gains of (+0.08261), (+0.0257) for XLNet and (+0.0087), (+0.0083) for ALBERT. Figure 8 presents a graphical analysis reflecting the variations in ROUGE F1 scores observed for the CNN/DailyMail dataset and three-word abstracts. The results indicate that the proposed model yields notable performance improvements with XLNet and ALBERT on the CNN/DailyMail dataset. While DistilBERT also showed positive gains, slight performance drops were observed for RoBERTa and Pegasus. Table 6 presents the processing times of various transformer-based LLM models across two datasets (DUC and CNN/DailyMail) and three summary lengths (200 words, 400 words, and 3 words). Each cell displays the model's processing times before and after applying the proposed summarization approach, along with the percentage reduction (gain). Notably, time reductions were observed across all models. Among these, ELECTRA demonstrated the fastest processing speeds, while Pegasus exhibited the highest percentage gains. Due to its large size and computational complexity, Pegasus also had the longest initial processing times. Nevertheless, the proposed framework yielded a remarkable 63.1% reduction in processing time for three-word summaries. DistilBERT, as a compressed LLM model, initially displayed shorter processing times; however, it achieved a 57.69% time reduction for 200-word summaries with the proposed approach.