Gated Graph Neural Attention Networks for abstractive summarization

Zeyu Liang; Junping Du; Yingxia Shao; Houye Ji

doi:10.1016/j.neucom.2020.09.066

ScienceGate Book Chapters

JOURNAL ARTICLE

Gated Graph Neural Attention Networks for abstractive summarization

Zeyu Liang Junping Du Yingxia Shao Houye Ji

Year: 2020 Journal: Neurocomputing Vol: 431 Pages: 128-136 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2020.09.066

Get Full-Text PDF Get Analytical Report

Keywords:

Automatic summarization Computer science Graph Artificial intelligence Artificial neural network Sequence (biology) Theoretical computer science Pattern recognition (psychology)

Metrics

Cited By

2.79

FWCI (Field Weighted Citation Impact)

Refs

0.91

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Natural Language Processing Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Gated Graph Neural Attention Networks for abstractive summarization

Metrics

Citation History

Topics

Related Documents

Abstractive Summarization Using Gated Graph Attention Networks

Gated Graph Neural Networks with Attention for Abstractive Summarization of Scientific Documents

Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization

A Local Attention-based Neural Networks for Abstractive Text Summarization

Incorporating word attention with convolutional neural networks for abstractive summarization