SUMSUG: Augmented Abstractive Text Summarization Model with Semantic Understanding Graphs
Pages 143 - 147
Abstract
As a research hotspot in natural language processing, automatic text summarization has been greatly developed. A summary should be a generalization based on depth understanding of the text. However, the existing models should not understand the semantic information of the text, so the generated summaries deviate from the semantics of the text and have low accuracy. This paper proposes an Augmented Abstractive Text Summarization Model with Semantic Understanding Graphs (SUMSUG). The model uses dual encoders, a text encoder and a graph encoder to guide the generation of summaries. And it obtains the features of the context from the text encoder and the structure features from the graph encoder. By fusing them the model obtains fuller semantic information. We evaluate the model on the Gigaword dataset. The experimental results show that the model performs better than other models, which prove the effectiveness of our model.
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Index Terms
- SUMSUG: Augmented Abstractive Text Summarization Model with Semantic Understanding Graphs
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
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Association for Computing Machinery
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Publication History
Published: 16 May 2023
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- Research-article
- Research
- Refereed limited
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- Key Research and Development Program of Shaanxi Province
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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