Abstract
In this paper, we propose a multi-roles graph model for extractive single-document summarization. In our model, we consider that each text can be expressed in some important words which we call roles. We design three roles, including noun role, verb role and numeral role, and build a multi-roles graph according to these three roles to represent a text. And then we project this graph into three single role graphs according to the role of nodes. After that, we extract some import features from these four graphs by applying a modified PageRank algorithm and then combine them with some statistical features such as sentence position and the length of sentence to represent each sentence. Finally we train a random forest model to learn the pattern of selecting important sentences to generate summaries. To evaluate our model, we perform some experiments on DUC2001 and DUC2002 and achieve 13.9% improvement over latest methods. Besides, we also obtain best results in ROUGE-2 compared with some classic methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fattah, M.A.: A hybrid machine learning model for multi-document summarization. Appl. Intell. 40(4), 592–600 (2014)
Luhn, H.P.: The automatic creation of literature abstracts. IBM Corp (1958)
Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1–66 (2017)
Chan, S.W.K.: Beyond keyword and cue-phrase matching: a sentence-based abstraction technique for information extraction. Decis. Support Syst. 42(2), 759–777 (2006)
Ye, S., Chua, T.S., Kan, M.Y., Qiu, L.: Document concept lattice for text understanding and summarization. Inf. Process. Manage. 43(6), 1643–1662 (2007)
Carenini, G., Ng, R.T., Zhou, X.: Summarizing emails with conversational cohesion and subjectivity. In: ACL 2008, Proceedings of the, Meeting of the Association for Computational Linguistics, June 15–20, 2008, Columbus, Ohio, USA, pp. 353–361. DBLP (2008)
Antiqueira, L., Oliveira Jr., O.N., da Fontoura Costa, L., das Graças Volpe Nunes, M.: A complex network approach to text summarization. Inf. Sci. 179(5), 584–599 (2009)
Alguliev, R.M., Aliguliyev, R.M., Hajirahimova, M.S., Mehdiyev, C.A.: Mcmr: maximum coverage and minimum redundant text summarization model. Expert Syst. Appl. 38(12), 14514–14522 (2011)
Ouyang, Y., Li, W., Zhang, R., Li, S., Lu, Q.: A progressive sentence selection strategy for document summarization. Inf. Process. Manage. 49(1), 213–221 (2013)
Yang, L., Cai, X., Zhang, Y., Shi, P.: Enhancing sentence-level clustering with ranking-based clustering framework for theme-based summarization. Inf. Sci. 260(1), 37–50 (2014)
Fang, H., Lu, W., Wu, F., Zhang, Y., Shang, X., Shao, J., et al.: Topic aspect-oriented summarization via group selection. Neurocomputing 149, 1613–1619 (2015)
Parveen, D., Strube, M.: Integrating importance, non-redundancy and coherence in graph-based extractive summarization. In: International Conference on Artificial Intelligence, pp. 1298–1304. AAAI Press (2015)
Li, W.: Abstractive multi-document summarization with semantic information extraction. In: Conference on Empirical Methods in Natural Language Processing, pp. 1908–1913 (2015)
Fattah, M.A., Ren, F.: Ga, mr, ffnn, pnn and gmm based models for automatic text summarization. Comput. Speech Lang. 23(1), 126–144 (2009)
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A Meeting of Sigdat, A Special Interest Group of the Acl, Held in Conjunction with ACL 2004, 25–26 July 2004, Barcelona, Spain, pp. 404–411. DBLP (2004)
Mendoza, M., Bonilla, S., Noguera, C., Cobos, C., Elizabeth, N.: Extractive single-document summarization based on genetic operators and guided local search. Expert Syst. Appl. 41(9), 4158–4169 (2014)
Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: Meeting of the Association for Computational Linguistics, pp. 484–494 (2016)
Wan, X.: Towards a unified approach to simultaneous single-document and multi-document summarizations, pp. 1137–1145 (2010)
Shen, D., Sun, J. T., Li, H., Yang, Q., Chen, Z.: Document summarization using conditional random fields. In: International Joint Conference on Artifical Intelligence, pp. 2862–2867. Morgan Kaufmann Publishers Inc. (2007)
Alguliyev, R.M., Aliguliyev, R.M., Isazade, N.R., Abdi, A., Idris, N.: A model for text summarization. Int. J. Intell. Inf. Technol. 13(1), 67–85 (2017)
Acknowledgments
The research was supported in part by NSFC under Grant Nos. 61572158 and 61602132, and Shenzhen Science and Technology Program under Grant Nos. JCYJ20160330163900579 and JSGG20150512145714247, Research Award Foundation for Outstanding Young Scientists in Shandong Province, (Grant No. 2014BSA10016), the Scientific Research Foundation of Harbin Institute of Technology at Weihai (Grant No. HIT(WH)201412).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, Z., Ye, Y., Xu, X., Li, F. (2017). Multi-roles Graph Based Extractive Summarization. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-70087-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70086-1
Online ISBN: 978-3-319-70087-8
eBook Packages: Computer ScienceComputer Science (R0)