Existing code summarization approaches primarily leverage Abstract Syntax Trees (ASTs) and sequential information from source code to generate code summaries while often overlooking the critical considerations of dependencies among code elements and code hierarchy. However, effective summarization necessitates a holistic analysis of code snippets from three distinct aspects: lexical, syntactic, and semantic information. Large language models recently have shown remarkable performance across various tasks, yet their capability of capturing features of code has not been officially demonstrated. In this paper, we propose a novel code summarization approach utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs adeptly capture essential code features at lexical, syntactic, and semantic levels within a hierarchical structure. Our HierarchyNet processes each layer of the HCR separately, employing a Heterogeneous Graph Transformer, a Tree-based CNN, and a Transformer Encoder. In addition, our approach demonstrates superior performance compared to fine-tuned pre-trained models, including CodeT5, and CodeBERT, as well as large language models that employ zero/few-shot settings, such as StarCoder and CodeGen. Implementation details can be found at https://anonymous.4open.science/r/HierarchyNet-41FC.