## 5 Experiment Setup and Results
### Experimental Results
Experimental results on the DUC dataset for 200-word summaries show that the proposed framework significantly outperforms previously evaluated LLMs, with the exception of ELECTRA. One of the key strengths of the model lies in its ability to generate more meaningful and contextually consistent candidate summaries. In terms of ROUGE-1, which measures unigram overlap, the framework yields notable F1 score improvements: +0.0097 for ALBERT, +0.0146 for RoBERTa, +0.0229 for DistilBERT, and +0.0359 for Pegasus. The model also demonstrates strong performance in preserving contextual semantic integrity, as reflected by ROUGE-2 scores. Improvements include +0.0383 for ALBERT, +0.0108 for RoBERTa, +0.0185 for DistilBERT, and +0.0425 for Pegasus. ROUGE-L results, which emphasize the preservation of longer sentence structures, further confirm the model's effectiveness. The proposed approach reports F1 gains of +0.0192 for ALBERT, +0.0099 for RoBERTa, +0.0301 for DistilBERT, and +0.0372 for Pegasus. The results also indicate an improvement in computational efficiency. According to the ROUGE-W metric, which captures weighted sequence overlap, the proposed model yields F1 score gains of +0.0067 for ALBERT, +0.0156 for XLNet, and +0.0198 for Pegasus. In terms of contextual ordering and sentence consistency, evaluated through the ROUGE-S and ROUGE-SU metrics, the model reports improvements of +0.0082 and +0.0081 for ALBERT and +0.0332 for Pegasus. Figure 7 provides a graphical summary of the ROUGE-F1 score variations for the DUC dataset with 200-word summaries. Experiments on the CNN/DailyMail dataset with three-word summaries reveal that the proposed model achieves notable performance gains, particularly when used with XLNet, DistilBERT, and ALBERT. According to the ROUGE-1 metric, which measures word-level overlap, F1 score improvements were observed as follows: +0.0578 for XLNet, +0.1321 for DistilBERT, and +0.0361 for ALBERT. In contrast, the model showed limited or no improvement with RoBERTa and Pegasus. These results indicate that the framework performs especially well with models such as XLNet and ALBERT. Figure 6: Graphical analysis reflecting the observed variations in ROUGE F1 scores for the DUC dataset and 400-word summaries. \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—400-Word Abstracts)**} \\ \cline{2-13}  & & \multicolumn{3}{c}{**XLNet**} & \multicolumn{3}{c}{**Roberts**} & \multicolumn{3}{c}{**DistilBERT**} & \multicolumn{1}{c}{**Electra**} & \multicolumn{3}{c}{**Albert**} & \multicolumn{3}{c}{**Pegauss**} \\  & & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**Prop. **} & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**Prop. **} & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**Prop. **} & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**Prop. **} & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**Prop. **} \\ \hline  & R & 0.48489 & **0.49119A** & 0.45074 & **0.501214** & 0.48725 & **0.49911A** & 0.44350 & **0.49335A** & 0.47411 & **0.53538A** & 0.50153 & **0.52736A** \\  & & (+0.003) & (+0.005) & (+0.005) & (+0.0118) & (+0.0048) & (+0.00612) & (+0.0258) & (+0.0258) & (+0.03731) & (+0.03892A) \\  & & P & 0.47843 & **0.49864** & 0.45095 & **0.49553A** & 0.48447 & **0.49546A** & 0.44173 & **0.49230A** & 0.47534 & **0.52823A** & 0.49371 & **0.52092A** \\  & & (+0.0110) & (+0.0035) & (+0.0010) & (+0.0050) & (+0.0052) & (+0.0052) & (+0.0331) & (+0.0331) & (+0.0331) & \\  & & & (+0.00184) & 0.45067 & **0.49519A** & 0.48565 & **0.49422** & 0.42729 & **0.47468** & 0.49815A** & 0.49751 & **0.49239A** \\  & & & (+0.0086) & (+0.0445) & (+0.0109) & (+0.0050) & (+0.0050) & (+0.0568) & (+0.02942) & (+0.02942) & \\  & & & & & & & & & & & & \\ \multirow{-4}{*}{ \begin{tabular}{} \end{tabular}