## 2 Related Works  
This section reviews academic studies that are directly or indirectly related to the proposed framework. The analysis aims to identify prevailing trends in the literature and to assess potential research gaps from a comprehensive perspective. Understanding these foundations is essential for positioning the current study within the broader scientific context. For clarity and thematic coherence, the literature is categorized into four main groups: (1) limitations in integrating graph structures with LLMs, (2) graph-based approaches to text summarization, (3) hybrid architectures combining LLMs with Graph Neural Networks (GNNs), and (4) graph-based models developed specifically for scientific text summarization. One major limitation of LLMs in handling graph-based data is their inability to process graph structures directly. Nevertheless, integrating graph learning models with LLMs has shown promise for enhancing their performance. For instance, GraphLLM [12] incorporates structural information from graphs into the input of LLMs, achieving a 96.45% reduction in context length. However, its effectiveness is highly dependent on the choice of graph model and the capacity of the LLM, necessitating adaptation for different graph types. In [13], the authors explored node classification using graphs with textual node features. LLMs were used to enrich node representations, which were then processed by a GNN. While this approach leveraged LLMs for improved feature encoding, it also introduced a significant computational overhead. Moreover, over-reliance on the graph structure led to the omission of relational information between nodes. In another study [14], various graph-to-text transformation methods were evaluated to determine their impact on extractive summarization performance. With appropriate representations, performance improvements of up to 61.8% were achieved. However, the results did not generalize well to large-scale graphs, indicating limitations in scalability. In [15], a benchmark was introduced to assess the structural and semantic understanding of LLMs of graphs. The input design and context structure were identified as key factors affecting performance. The study found that LLMs still perform worse than graph-specialized models. Studies integrating graph-based text summarization typically utilize relationships at the document and sentence levels. GoSum [16] converts each document into a heterogeneous graph, modeling sentence-level and hierarchical contexts with GNNs before applying LLMs. The model achieved high ROUGE scores on the PubMed and arXiv datasets, but its performance depends heavily on the document structure. MultiBART-GAT (Graph Attention Network) [17] is another abstractive summarization model that combines knowledge graphs with BART. In this study, the limitations of LLMs in checking the accuracy of information were addressed. It leverages knowledge graphs and transformers for abstractive summarization. The researchers proposed a BART-based model called MultiBART-GAT. This model integrated knowledge graphs and multi-source transformers. LLMs were effective in controlling the information accuracy. The model's knowledge graph creation and multi-source data processing are computationally expensive and complex. The HeterMDS model [18] constructs a heterogeneous graph structure consisting of document, paragraph, and sentence nodes. It employs a Graph Attention Network (GAT) to learn, while maintaining structural and content integrity, aiming to produce high-quality summaries. The combination of GNNs and LLMs shows a strong potential for summarization and other NLP tasks. In [19], GNNs were used to predict protein interactions, while LLMs validated these predictions. This approach achieved a high accuracy and provided meaningful explanations. The study in [20] focused on modeling rich conversational structures, addressing the challenges caused by the unstructured nature of human interactions. Two graph types, Discourse Relationship Graph and Action Graph, were employed.