Long document summarization generation faces challenges such as capturing inter-sentence relationships， long-range dependencies， and efficient encoding and extraction of document information， which has always been a difficult task in the field of natural language processing. At the same time， scientific documents， characterized by multiple chapters and paragraphs with complex hierarchical structures， further increases the difficulty of the summarization task of scientific documents. To address these issues， a scientific document Summarization model based on Multi-Graph Neural Network （GNN） and Graph Contrastive Learning （GCL） （MGCSum） was proposed. Firstly， for a given input document， intra-sentence and inter-sentence relationships were modeled by using homogeneous GNN and heterogeneous GNN， respectively， so as to generate initial sentence representations. Secondly， these sentence representations were fed into a multi-head HyperGraph ATtention network （HGAT）， where self-attention mechanisms were used to fully capture relationships between nodes and edges， thereby updating and learning inter-sentence representations. Thirdly， a GCL module was introduced to enhance global topic awareness， thereby improving the semantic consistency and discriminability of sentence representations. Finally， a Multi-Layer Perceptron （MLP） and a normalization layer were applied to calculate a score for determining whether a sentence should be selected for summarization. Experimental results on the PubMed and ArXiv datasets indicate that the MGCSum outperforms most baseline models. Specifically， on the PubMed dataset， MGCSum achieves the ROUGE-1， ROUGE-2， and ROUGE-L of 48.97%， 23.15%， and 44.09%， respectively， with improvements of 0.20， 0.71， and 0.26 percentage points， respectively， compared to the existing state-of-the-art model HAESum （Hierarchical Attention graph for Extractive document Summarization）. It can be seen that by integrating multi-GNN and GCL， MGCSum captures hierarchical structural information and inter-sentence relationships more effectively， enhancing the accuracy and semantic consistency of summarization， and demonstrating its advantages in scientific document summarization tasks.