← Back to papers
2022 COLING COLING 2022

A Survey of Automatic Text Summarization Using Graph Neural Networks

Abstract

AbstractAlthough automatic text summarization (ATS) has been researched for several decades, the application of graph neural networks (GNNs) to this task started relatively recently. In this survey we provide an overview on the rapidly evolving approach of using GNNs for the task of automatic text summarization. In particular we provide detailed information on the functionality of GNNs in the context of ATS, and a comprehensive overview of models utilizing this approach.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Marco Ferdinand Salchner , Adam Jatowt

Topics

Machine Learning > Core Methods > Representation Learning Deep Learning > Architectures > Graph Neural Networks Deep Learning > Models > Generative Models

Keywords

natural language processing text summarization automatic summarization graph neural network
Download PDF

Related papers

MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation 2022
The Role of Context and Uncertainty in Shallow Discourse Parsing 2022
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training 2022
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification 2022
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories 2022