Structured Neural Summarization

2018/11/05 by Patrick Fernandes, Miltiadis Allamanis, Fernandes, Patrick +3 · 4 citations
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Software Engineering (cs.SE) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1811.01824

openalex publication_date 2018/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

Citations

• Teaching Machines to Read and Comprehend
• Gated Graph Sequence Neural Networks
• Neural Message Passing for Quantum Chemistry
• CodeSum: Translate Program Language to Natural Language
• Deep Learning to Detect Redundant Method Comments
• The Natural Language Decathlon: Multitask Learning as Question Answering
• Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
• A Convolutional Attention Network for Extreme Summarization of Source Code
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

Cited by

• Readability-Robust Code Summarization via Meta Curriculum Learning
• Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations
• Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
• Code Structure–Guided Transformer for Source Code Summarization

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