## 5 Experiment Setup and Results
### Experimental Results  
This section presents performance analyses based on the application of the proposed text summarization framework to the DUC and CNN/DailyMail. To provide LLMs with both conceptual and structural information, the DUC and CNN/DailyMail datasets were used as input to the integrated framework. The texts were preprocessed to preserve their structural and contextual integrity and transformed into simplified, structured forms. Graphs were then constructed from sentence-level phrases and key terms extracted from the processed texts. Using graph-based MIS theory, nodes deemed less important were identified and removed. This yielded new graphs composed of structurally inclusive and semantically rich nodes. Node centrality measures were applied to select the most significant nodes, and their corresponding sentences were identified. These selected sentences were further scored by pre-trained LLMs (XLNet, RoBERTa, DistilBERT, ELECTRA, ALBERT, and Pegasus) to determine final summary candidates. This process resulted in refined summaries that integrate both structural and conceptual dimensions. By combining MIS-based graph filtering with LLM evaluation, the framework significantly improves the summarization performance in terms of both accuracy and computational efficiency. The framework's effectiveness was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-W, ROUGE-S, and ROUGE-SU metrics, along with sub-metrics such as Precision, Recall, and F-measure. Comparative analyses were conducted on both datasets to validate the results:  
The framework achieved notable performance gains on the DUC dataset compared to the baseline LLMs used in the experiments. Its effectiveness in summarizing current events was further evaluated using the CNN/DailyMail dataset. To assess its summarization capabilities across different summary lengths, the model was tested on both short and long summaries. This evaluation aimed to measure the model's ability to retain key information in shorter outputs and preserve a detailed context in longer ones. Table 3 presents the ROUGE scores for 400-word summaries, comparing the proposed framework with baseline LLMs. The F1 score, which balances Precision and Recall, serves as a more reliable performance metric, especially in scenarios involving class imbalance. Therefore, model performance in this study is primarily reported using the F1 score, calculated as the harmonic mean of Precision and Recall. Experiments conducted on the DUC dataset with 400-word summaries demonstrate that the proposed framework consistently outperforms baseline LLMs. The most significant gain was observed for ALBERT, while all models including ELECTRA showed statistically meaningful improvements. According to the ROUGE-1 metric, which measures unigram overlap, the model achieved notable F1 increases for ALBERT (+0.0568), ELECTRA (+0.0504), RoBERTa (+0.0445), DistilBERT (+0.0109), Pegasus (+0.0294), and XLNet (+0.0086). The ROUGE-2 results, which reflect bigram-level contextual alignment, indicate improved consistency in ALBERT (+0.0678), ELECTRA (+0.0462), RoBERTa (+0.0385), Pegasus (+0.0332), XLNet (+0.0233), and DistilBERT (+0.0001). With ROUGE-L, which emphasizes the preservation of long sentence structures, the proposed model again showed strong gains: ALBERT (+0.0612), ELECTRA (+0.0498), RoBERTa (+0.0331), DistilBERT (+0.0071), XLNet (+0.0094), and Pegasus (+0.0015). ROUGE-W, which evaluates weighted sequence overlap and thus indicates the semantic richness of generated summaries, showed performance gains across all models--highest in ALBERT (+0.0258) and ELECTRA (+0.0223). For phrase-level continuity, as measured by ROUGE-S and ROUGE-SU, the framework improved contextual fluency in ALBERT (+0.0537), RoBERTa (+0.0380), ELECTRA (+0.0329), Pegasus (+0.0260), XLNet (+0.0092), and DistilBERT (+0.0054). Figure 6 provides a visual representation of ROUGE-F1 score improvements across models for the DUC dataset. Table 4 summarizes the 200-word ROUGE scores of the proposed model compared to the baseline LLMs.