HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs effectively capture essential code features at lexical, syntactic, and semantic levels by abstracting coarse-grained code elements and incorporating fine-grained program elements in a hierarchical structure. Our HierarchyNet method processes each layer of the HCR separately through a unique combination of the Heterogeneous Graph Transformer, a Tree-based CNN, and a Transformer Encoder. This approach preserves dependencies between code elements and captures relations through a novel Hierarchical-Aware Cross Attention layer. Our method surpasses current state-of-the-art techniques, such as PA-Former, CAST, and NeuralCodeSum.
Code (0)
등록된 구현이 없습니다.
Tasks
Clone DetectionCode ClassificationCode CompletionCode SummarizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between …
Extractive SummarizationExtractive Text SummarizationNode ClassificationSentence+1Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts. However, construction and mainten…
ClusteringLanguage ModelingLanguage ModellingText SummarizationA Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while pr…
ClusteringGraph ClassificationGraph EmbeddingGraph Learning+4Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges wh…
Graph Representation LearningRepresentation LearningVariational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to …
Imputation