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 cstr: 32379.14.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)

References

LYNN H M, CHOI C, and KIM P. An improved method of automatic text summarization for web contents using lexical chain with semantic-related terms[J]. Soft Computing, 2018, 22(12): 4013–4023. doi: 10.1007/s00500-017-2612-9

SHETTY K and KALLIMANI J S. Automatic extractive text summarization using K-means clustering[C]. International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, India, 2017: 1–9.

YU Shanshan, SU Jindian, LI Pengfei, et al. Towards high performance text mining: A TextRank-based method for automatic text summarization[J]. International Journal of Grid and High Performance Computing (IJGHPC) , 2016, 8(2): 58–75. doi: 10.4018/IJGHPC.2016040104

NGUYEN-HOANG T A, NGUYEN K, and TRAN Q V. TSGVi: A graph-based summarization system for Vietnamese documents[J]. Journal of Ambient Intelligence and Humanized Computing, 2012, 3(4): 305–313. doi: 10.1007/s12652-012-0143-x

KHAN A, SALIM N, FARMAN H, et al. Abstractive text summarization based on improved semantic graph approach[J]. International Journal of Parallel Programming, 2018: 1–25. doi: 10.1007/s10766-018-0560-3

BANARESU L, BONIAL C, CAI S, et al. Abstract meaning representation for sembanking[C]. Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, Sofia, Bulgaria, 2013: 178–186.

LIU Fei, FLANIGAN J, THOMSON S, et al. Toward abstractive summarization using semantic representations[C]. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, USA, 2015: 1077–1086.

SONG Linfeng, PENG Xiaochang, ZHANG Yue, et al. AMR-to-text generation with synchronous node replacement grammar[C]. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017: 7–13.

KONSTAS I, IYER S, YATSKAR M, et al. Neural AMR: Sequence-to-sequence models for parsing and generation[C]. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017: 146–157.

明拓思宇, 陈鸿昶, 黄瑞阳, 等. 一种基于加权AMR图的语义子图预测摘要算法[J]. 计算机工程, 2018, 44(10): 292–297. doi: 10.19678/j.issn.1000-3428.0050770

MING Tuosiyu, CHEN Hongchang, HUANG Ruiyang, et al. A semantic subgraph predictive summary algorithm based on improved AMR graph[J]. Computer Engineering, 2018, 44(10): 292–297. doi: 10.19678/j.issn.1000-3428.0050770

COLLINS M. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms[C]. Proceedings of the ACL-02 conference on Empirical Methods in Natural Language Processing, Philadelphia, USA, 2002, 10: 1–8.

HERMANN K M, KOČISKÝ T, GREFENSTETTE E, et al. Teaching machines to read and comprehend[C]. Proceeding NIPS’15 Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015, 1: 1693–1701.

LIN Chinyew. ROUGE: A package for automatic evaluation of summaries[C]. Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, Barcelona, Spain, 2004, 10: 74–81.

CAI Shu and KNIGHT K. Smatch: An evaluation metric for semantic feature structures[C]. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 2013, 2: 748–752.

SEE A, LIU P J, and MANNING C D. Get to the point: Summarization with pointer-generator networks[C]. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, 1: 1073–1083.

TAN Jiwei, WAN Xiaojun, and XIAO Jianguo. Abstractive document summarization with a graph-based attentional neural model[C]. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, 1: 1171–1181.

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