Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming

Hongchang CHEN; Tuosiyu MING; Shuxin LIU; Chao GAO

doi:10.11999/JEIT180720

Volume 41 Issue 7

Jul. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(7): 1674-1681

Hongchang CHEN, Tuosiyu MING, Shuxin LIU, Chao GAO. Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1674-1681. doi: 10.11999/JEIT180720

Citation:

Hongchang CHEN, Tuosiyu MING, Shuxin LIU, Chao GAO. Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1674-1681. doi: 10.11999/JEIT180720

Citation:

Hongchang CHEN, Tuosiyu MING, Shuxin LIU, Chao GAO. Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1674-1681. doi: 10.11999/JEIT180720

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Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming

doi: 10.11999/JEIT180720

National Digital Switching System Engineering Technological Research Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61521003), The National Natural Science Foundation of China Youth Science Fund (61601513)

Received Date: 2018-07-18
Rev Recd Date: 2018-10-26

Available Online: 2018-11-19

Publish Date: 2019-07-01

Abstract

Abstract

In order to solve the incomplete semantic structure problem that occurs in the process of using the Abstract Meaning Representation (AMR) graph to predict the summary subgraph, a semantic summarization algorithm is proposed based on Integer Linear Programming (ILP) reconstructed AMR graph structure. Firstly, the text data are preprocessed to generate an AMR total graph. Then the important node information of the summary subgraph is extracted from the AMR total graph based on the statistical features. Finally, the ILP method is applied to reconstructing the node relationships in the summary subgraph, which is further utilized to generate a semantic summarization. The experimental results show that compared with other semantic summarization methods, the ROUGE index and Smatch index of the proposed algorithm are significantly improved, up to 9% and 14% respectively. This method improves significantly the quality of semantic summarization.
- Abstract Meaning Representation (AMR) graph,
- Semantic summarization,
- Summary subgraph,
- Semantic structure,
- Integer Linear Programming (ILP)

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References(16)

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