## 7 Conclusions  
The primary goal of this study is to address structural limitations in LLM-based text summarization and improve the accuracy, coherence, and efficiency of the generated summaries. Traditional LLMs emphasize contextual representations while neglecting structural features, often resulting in repetition, loss of context, and semantic drift, especially in long or complex texts. To overcome these issues, this study proposes a method to help LLMs produce more informative and reliable summaries.  
To achieve this, a framework is proposed that represents texts as graphs and uses the MIS algorithm to filter sentences for more information-dense content. The method converts texts into sentence-level graph structures and passes the selected sentences to LLMs for summarization. Experimental results show clear improvements in ROUGE metrics. Processing time and energy efficiency also improved significantly.  
A potential risk of the MIS algorithm is its tendency to overlook contextual information during sentence selection. In the proposed framework, this is addressed by modeling semantic relationships between sentences using graph structures. The MIS algorithm selects central, information-dense, and mutually dissimilar sentences to preserve the summary's semantic scope. Performance gains in ROUGE-L and ROUGE-W, both sensitive to sequence and semantic consistency, indicate that contextual integrity is maintained. These results warrant a closer examination of how the MIS-based approach differs from prior graph-based summarization methods. Its novelty lies in structurally filtering content through MIS theory rather than relying on node centrality or attention weights. Instead, it identifies non-adjacent and thus semantically independent sentence clusters. This process removes irrelevant or weakly connected content, improving coherence and consistency while reducing LLMs' processing load. The model-agnostic design also allows its integration with a wide range of Large Language Models. The experimental results confirm the value of incorporating structural information in graph-based summarizations.  
The proposed framework also has limitations. In very short summaries, MIS-based filtering may exclude important contextual expressions, narrowing the informational scope and affecting semantic integrity, especially in real-time applications such as chatbots. Although only two datasets (DUC-2002 and CNN/DailyMail) were used, CNN/DailyMail includes texts from diverse domains (politics, health, sports, and economics), offering a degree of topic generalizability. Future work will explore applications in more technical domains, such as legal and biomedical texts. To assess its consistency, fluency, and informativeness, further analyses supported by human evaluation are planned. This will enable a more robust validation of the framework from both quantitative and qualitative perspectives.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
LLM Large Language Model  
NLP Natural Language Process  
MIS Maximum Independent Set