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MuchSUM: Multi-channel Graph Neural Network for Extractive ..:

, In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval,

Mao, Qianren ; Zhu, Hongdong ; Liu, Junnan... - p. 2617-2622 , 2022

Link: https://dl.acm.org/doi/10.1145/3477495.3531906

RT 
T1 <cTitle>Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval</cTitle>: T1 <Title>MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3531906&Exemplar=1&LAN=DE
				
A1 Mao, Qianren
A1 Zhu, Hongdong
A1 Liu, Junnan
A1 Ji, Cheng
A1 Peng, Hao
A1 Li, Jianxin
A1 Wang, Lihong
A1 Wang, Zheng
PB ACM
YR 2022
K1 bipartite word-sentence heterogeneous graph
K1 multi-channel graph
K1 text summarization
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Unsupervised learning and clustering
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Mathematics of computing
K1 Discrete mathematics
K1 Graph theory
K1 Hypergraphs
SP 2617
OP 2622
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3477495.3531906
DO https://dl.acm.org/doi/10.1145/3477495.3531906
SF ELIB - SuUB Bremen

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