JOURNAL ARTICLE

Single Document Extractive Summarization Model Based on Heterogeneous Graph Transformer

Ling GanPeng He

Year: 2022 Journal:   Journal of Physics Conference Series Vol: 2171 (1)Pages: 012012-012012   Publisher: IOP Publishing

Abstract

Abstract At present, the graph model-based summary model has problems such as insufficient semantic fusion between nodes and lack of location information. Therefore, this paper proposes a single-document extraction text summary model based on a heterogeneous graph attention neural network, using HGT (Heterogeneous Graph Transformer), Heterogeneous Graph Attention Neural Network to solve the defect of insufficient deep semantic fusion of nodes, and use trainable position coding to solve the defect of missing position information. Experiments show that the model in this paper has improved on the three evaluation indicators of R_1, R_2 and R_L, and the abstracts extracted have better generality.

Keywords:
Computer science Automatic summarization Graph Transformer Generality Artificial intelligence Data mining Theoretical computer science

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Topics

Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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