## 5 Experiment Setup and Results
### Experimental Results
These findings suggest that the framework is effective for both large-scale and compressed models. ELECTRA, recognized as one of the fastest baseline models, also demonstrated gains of up to 58.56%. The most striking improvement was observed with ALBERT, which achieved a 60.22% reduction in processing time for DUC-200 and a 51.85% reduction for short text summara  
Figure 7: Graphical analysis reflecting the observed variations in ROUGE F1 scores for the DUC dataset and 200-word summaries. rization, making it the most efficient model under the proposed framework. Figure 9 illustrates the time improvements observed across the different transformer-based LLM models, datasets, and summarization lengths. Figure 8: Graphical analysis reflecting the observed variations in ROUGE-F1 scores for the CNN/DailyMail dataset. Figure 9: Effect of the proposed framework on processing time. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**ROUGE Performance Values (CNN/DailyMail—3-Word Abstracts)**} \\ \cline{3-13}  & & \multicolumn{2}{c}{**XLNet**} & \multicolumn{2}{c}{**Robertsa**} & \multicolumn{2}{c}{**Distiltilbert**} & \multicolumn{2}{c}{**Electra**} & \multicolumn{2}{c}{**Albert**} & \multicolumn{2}{c}{**Pegasus**} \\  & & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** \\ \hline \multirow{11}{*}{**Model**} & R & 0.20274 & **0.33916A** & 0.31928 & 0.29687 & 0.34964 & 0.39377A & 0.39918 & 0.38935 & 0.25092 & 0.34891A & 0.34134 & 0.32971 \\  & & & (+0.1364) & & & (+0.0441) & & & (+0.0980) & & (+0.0980) & \\ \cline{1-1}  & P & 0.17316 & **0.18269A** & 0.20929 & 0.17842 & 0.21146 & 0.38293A & 0.40108 & 0.39035 & 0.18516 & 0.18799A & 0.22404 & 0.17474 \\  & & & (+0.0095) & & & (+0.0174) & & & (+0.0174) & & (+0.0028) & \\ \cline{1-1}  & F1 & 0.17462 & **0.23245A** & 0.24024 & 0.21643 & 0.25597 & 0.38811A & 0.39980 & 0.38936 & 0.20367 & 0.23983A & 0.25771 & 0.22518 \\  & & & (+0.0578) & & & (+0.1321) & & & & (+0.0361) & \\ \hline \multirow{11}{*}{**Model**} & R & 0.02720 & **0.097933A** & 0.10391 & 0.06482 & 0.12666 & 0.10225 & 0.12258 & 0.09785 & 0.04476 & **0.092922** & 0.10747 & 0.08703 \\  & & & (+0.0701) & & & & (+0.0481) & & (+0.0481) & & (+0.0481) & \\ \cline{1-1}  & P & 0.02932 & **0.05161A** & 0.05958 & 0.04097 & 0.06796 & **0.09978A** & 0.12155 & 0.09786 & 0.03120 & **0.04988A** & 0.05933 & 0.04602 \\  & & & (+0.0222) & & & (+0.0318) & & (+0.0186) & & (+0.0186) & & (+0.0186) & \\ \cline{1-1}  & F1 & 0.02525 & **0.06587A** & 0.07303 & 0.04920 & 0.08628 & **0.10097A** & 0.12147 & 0.09776 & 0.03484 & **0.06367A** & 0.07460 & 0.09545 \\  & & & (+0.0406) & & & & (+0.0146) & & & (+0.0812) & \\ \cline{1-1}  & R & 0.15971 & **0.28513A** & 0.26122 & 0.24169 & 0.29066 & 0.27501 & 0.35715 & 0.35130 & 0.19718 & **0.27874A** & 0.28569 & 0.26888 \\  & & & (+0.0125) & & & & (+0.0125) & & & (+0.0812) & \\ \cline{1-1}  & P & 0.14087 & **0.15279A** & 0.16315 & 0.14827 & 0.17326 & 0.15642 & 0.35923 & 0.35256 & 0.14596 & **0.14947A** & 0.18835 & 0.13953 \\ \cline{1-1}  & F1 & 0.13901 & **0.19480A** & 0.19073 & 0.17801 & 0.21101 & 0.19464 & 0.35790 & 0.35148 & 0.16037 & **0.19068A** & 0.21624 & 0.18105 \\  & & & (+0.0557) & & & & (+0.0303) & & & (+0.0303) & & \\ \cline{1-1}  & R & 0.07475 & **0.12937A** & 0.12172 & 0.11413 & 0.13721 & 0.12538 & 0.12332 & 0.12067 & 0.09331 & **0.12843A** & 0.13270 & 0.12461 \\ \cline{1-1}  & P & 0.11091 & **0.11239A** & 0.12616 & 0.11453 & 0.13400 & 0.11726 & 0.19514 & 0.19063 & 0.11145 & **0.11186A** & 0.14386 & 0.10589 \\ \cline{1-1}  & F1 & 0.08302 & **0.11708A** & 0.11670 & 0.11035 & 0.13165 & 0.11770 & 0.15102 & 0.14762 & 0.09679 & **0.11641A** & 0.13138 & 0.11225 \\ \cline{1-1}  & & & (+0.0340) & & & & & & (+0.0196) & & \\ \hline \hline \end{tabular}
\end{table}