Long-context summarization is pivotal for extracting core insights from extensive documents.While Large Language Models (LLMs) show remarkable capabilities, they frequently encounter attention dilution and hallucination with lengthy inputs.Retrieval-Augmented Generation (RAG) partially mitigates this, but conventional RAG relies on shallow similarity retrieval of fragmented chunks, failing to capture high-level thematic structures and longrange dependencies.Although graph-based RAG approaches have emerged to address these structural limitations, existing solutions, such as Graph of Records (GoR), critically suffer from a fundamental flaw: they paradoxically re-introduce hallucinations by constructing graphs based on unreliable, LLM-generated responses.To overcome these challenges, we introduce Hierarchical Graph of Evidence (HiGoE) (Code link https://github.com/ tkw123/HiGOE).HiGoE redefines the retrieval process by replacing unreliable chunk-based methods with a filtered proposition-evidence graph, ensuring verifiable fact grounding and substantially reducing hallucination.Moreover, HiGoE leverages Personalized PageRank (PPR) to cluster related nodes into thematic hierarchies, thereby restoring global document structure and effectively mitigating attention dilution.To model complex, multi-level relations beyond mere shallow similarity, we develop an Enhanced Graph Attention Network.Experiments show HiGoE consistently surpasses baselines in quality and efficiency.

In the field of semantic segmentation, the limited receptive field of convolutional neural networks leads to insufficient extraction of global features, thereby affecting the accuracy of network segmentation. To address this issue, a Hierarchical Hybrid Encoder Network (HHEnet) based on Transformers is proposed for semantic segmentation. Firstly, to solve the problem of limited global feature information caused by the network’s limited receptive field, a Hierarchical Hybrid Encoder (HHE) is introduced, which consists of a Hierarchical Convolutional Encoder (HCE) and a Hierarchical Transformer Encoder (HTE). The encoder combines the advantages of convolution and transformers, allowing for effective extraction of both shallow and deep features. In order to further enhance spatial and global semantic information, the Feature Enhancement Module (FEM) was introduced, which consisted of two feature enhancement modules: spatial feature enhancement module (SEM) and global feature enhancement module (GEM), which enhanced spatial detail information and global semantic information respectively. Thus the accuracy of semantic segmentation can be improved. Finally, to alleviate the discrepancy between the features of the convolutional encoder and the transformer encoder, a Feature Guidance Module (FGM) is introduced. Experimental results conducted on Cityscapes, ADE20K and PASCAL VOC2012 datasets achieved mIoU scores of up to 81.9%, 49.4% and 79.1%, respectively. Compared to state-of-the-art networks, the research results confirm the higher segmentation accuracy of the proposed HHEnet in this study.