Abstract
Multi-language news event summarization aims to quickly obtain important information from lots of related news texts written in different languages automatically. Considering that the main expressed information for the same event is similar no matter what language it is presented, the paper proposes a novel unified approach to summarize important information from the monolingual and Chinese–Vietnamese bilingual news sets simultaneously. Firstly, analyzing the sentence dependence relationship, making rules to segment sentences into different grammatical parts, a bilingual dictionary is used to set up a bilingual feature space. Secondly, Chinese–Vietnamese sentence graph model is calculated distributively. Finally, using the features that graph nodes can boost each other and fusing context information, the sentences are ranked based on whether they can represent the important information. The experimental result shows that our method is effective.
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References
M. Gambhir, V. Gupta, Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1–66 (2017)
S. Chopra, M. Auli, A.M. Rush, Abstractive sentence summarization with attentive recurrent neural networks, in Proceedings of 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 (ACL, San Diego, 2016), pp. 93–98
K. Hong, J.M. Conroy, B. Favre, A. Kuesza, H. Lin, A. Nenkova, A repository of state of the art and competitive baseline summaries for generic news summarization, in Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014 (ELRA, Reykjavik, 2014), pp. 1608–1616
E. Baralis, L. Cagliero, N. Mahoto, A. Fiori, GRAPHSUM: discovering correlations among multiple terms for graph-based summarization. Inf. Sci. 249, 96–109 (2013)
H.P. Luhn, The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
D. Shen, J.T. Sun, H. Li, Q. Yang, Z. Chen, Document summarization using conditional random fields, in Proceedings of 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 (IJCAI, Morgan Kaufmann, Hyderabad, 2007), pp. 2862–2867
L. Li, K. Zhou, G.R. Xue, H. Zha, Y. Yu, Enhancing diversity, coverage and balance for summarization through structure learning, in Proceedings of the 18th International World Wide Web Conference, WWW 2009 (ACM, Madrid, 2009), pp. 71–80
X. Wan, Towards a unified approach to simultaneous single-document and multi-document summarizations, in Proceedings of the 23rd International Conference on Computational Linguistics, Coling 2010 (ACM, Beijing, 2010), pp. 1137–1145
G. Erkan, D.R. Radev, LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22(204), 457–479 (2004)
Y. Li, S. Li, Query-focused multi-document summarization: combining a novel topic model with graph-based semi-supervised learning, in Proceedings of the International Conference on Computational Linguistics, Coling 2014 (ACM, Dublin, 2014), pp. 1197–1207
J.A. Xu, J.M. Liu, K. Araki, A hybrid topic model for multi-document summarization. IEICE Trans. Inf. Syst. 98(5), 1089–1094 (2014)
X. Wan, H. Li, J. Xiao, Cross-language document summarization based on machine translation quality prediction, in Proceeding of the Annual Meeting of the Association for Computational Linguistics, ACL2010 (ACL, Uppsala, 2010), pp. 917–926
J.F. García, M.V. Carriegos, On parallel computation of centrality measures of graphs. J. Supercomput. 75(3), 1410–1428 (2019)
M. Nasir, K. Muhammad, J. Lloret, A.K. Sangaiah, M. Sajjad, Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. J. Parallel Distrib. Comput. 126, 161–170 (2019)
J. Samuel, X. Yuan, X. Yuan, B. Walton, Mining online full-text literature for novel protein interaction discovery. in Proceeding of International Workshop on Data Mining for High Throughput data from Genome-Wide Association Studies. IEEE Int’l Conf. on Bioinformatics & Biomedicine, Hong Kong, Dec 18–21, 2010
L. Gu, Y. Han, C. Wang, W. Chen, J. Jiao, X. Yuan, Module overlapping structure detection in PPI using an improved link similarity-based Markov clustering algorithm. Neural Comput. & Applic. 31(5), 1481–1490 (2018)
Y. Li, D. McLean, Z.A. Bandar, J.D. O’Shea, K. Crockett, Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18(8), 1138–1150 (2006)
C.Y. Lin, E. Hovy, Automatic evaluation of summaries using n-gram co-occurrence statistics, in Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2003 (NAACL, Edmonton, 2003), pp. 150–157
R. Mihalcea, P. Tarau, A language independent algorithm for single and multiple document summarization. Unt Sch. Works 2005, 19–24 (2005)
X. Yuan, J. Zhang, X. Yuan, B.P. Buckles, Multi-scale feature identification using evolution strategies. Image Vis. Comput. 23(6), 555–563 (2005)
Acknowledgments
The work was supported by National Natural Science Foundation of China (Grant Nos. 61972186, 61732005, 61761026, 61672271 and 61762056), National Key Research and Development Plan (Grant Nos. 2018YFC0830105, 2018YFC0830100), Yunnan high-tech industry development project (Grant No. 201606), Natural Science Foundation of Yunnan Province (Grant No. 2018FB104), and Talent Fund for Kunming University of Science and Technology (Grant No. KKSY201703005).
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Gao, S., Yu, Z., Li, Y., Wang, Y., Zhang, Y. (2020). Chinese–Vietnamese Bilingual News Event Summarization Based on Distributed Graph Ranking. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_8
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