## 2 Related Works
The model performed well in both automatic and human evaluations. In [21], document-level information was incorporated alongside sentence-level relationships using a graph-structured representation. This method improved the summarization quality, especially for long scientific articles, and worked well across different document lengths and types. However, the model's performance depended on dataset characteristics and required high integration costs. Some studies apply graph-based summarization techniques to specific domains. In this study, an innovative model is proposed by integrating neural networks and graph-based methods for a multi-document summarization task [22]. This model aims to create effective and meaningful summaries by calculating the relationships between sentences. The model has been tested on the DUC-2004 dataset. Document section hierarchies are represented in the graph's edges. Offline reinforcement learning is employed, and evaluations on PubMed and arXiv datasets yield high ROUGE scores. However, since the model selects sentences independently, context integrity is not provided. The desired level of semantic inference cannot be achieved due to repetitive sentences and heavy processing loads. In [23], the ability of LLMs to make sense of graph data and optimize network performance is focused on. The proposed framework aims to improve the route optimization and communication resource allocation of unmanned aerial vehicles. With LLM-supported graph analysis, a great potential has been achieved in network optimization and automatic decision-making processes. [PERSON] et al. [24] used graph structures for summarizing biomedical texts. In the generated graphs, semantic relationships between concepts were utilized to summarize important content. Additionally, nodes were classified using UMLS semantic types. This foundation was later extended by [PERSON] [25], whose new approach considered not only the semantic similarity between concepts but also their frequency of occurrence within documents. This led to more meaningful node connections and thus, the creation of more robust graphs. In a study where GNN processes were combined with text-basedguidance [26], language-based graph representations were used. The issue of out-of-vocabulary (OOV) tokens used in nodes hindering task transferability was addressed through a vocabulary approach. However, the prompt-based GNN processed each graph independently, neglecting semantic similarities across graphs. Despite significant progress in integrating graph structures with LLMs, several key limitations remain. The most notable is the high computational cost, as LLMs require considerable processing power due to their complexity. Another major drawback is the frequent inability to effectively combine structural and semantic information. Many models focus primarily on either structural relationships or conceptual content, with few successfully integrating both aspects. This study proposes a framework that simultaneously evaluates structural and semantic information, achieves a high performance with low computational cost, and combines the robust theoretical foundations of graph theory with LLMs. It aims to address the context loss and processing inefficiencies found in existing methods. The need for a general-purpose framework constitutes the primary motivation for this study. Table 1 presents the main features, advantages, and limitations of the reviewed studies, including the proposed method.