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New Technology of Library and Information Service  2013, Vol. 29 Issue (2): 43-49    DOI: 10.11925/infotech.1003-3513.2013.02.07
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Automatic Abstracting Generating Based on Mobile Short Message Text Information Flow
Liu Jinling1, Ni Xiaohong2, Wang Xingong2
1. Computer Engineering Faculty, Huaiyin Institute of Technology, Huaian 223003, China;
2. Department of Computer, Cangzhou Teachers College, Cangzhou 061001, China
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Abstract  Due to the characteristics of mobile short message text information flow in the practical application,an automatic digest generation model is designed. The model uses word co-occurrence to define the semantic similarity. Using the TF-IDF,weights of feature words and abstracts candidate sentence weights are defined in the model. By removing isolated points, the algorithm generates smaller redundancy and more readable short text messages flow digest according to the weight screening abstract and abstract sort. Experiments of the relevant data show that the model has better quality and higher efficiency in abstract generation.
Key wordsMobile short message text      Information flow      Abstracts      Weights     
Received: 23 August 2012      Published: 24 April 2013
:  TP391  

Cite this article:

Liu Jinling, Ni Xiaohong, Wang Xingong. Automatic Abstracting Generating Based on Mobile Short Message Text Information Flow. New Technology of Library and Information Service, 2013, 29(2): 43-49.

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[1] 12321 网络不良与垃圾信息举报受理中心.2011 年下半年手机短信息状况调查报告[R/OL].[2012-08-17]. sms1102.pdf.(12321 Report Center.Investigation Report of 2011 Second Half of Mobile Phone Short Message[R/OL]. [2012-08-17]. sms1102.pdf.)
[2] Carbonell J, Goldstein J. The Use of MMR, Diversity-based Reranking for Reordering Documents and Producing Summaries[C]. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM,1998:335-336.
[3] Lapata M.Automatic Evaluation of Information Ordering:Kendall's Tau[J].Computational Linguistics,2006,32(4):471-484.
[4] Hu M, Sun A, Lim E P. Comments-oriented Document Summarization: Understanding Documents with Readers' Feedback[C].In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA:ACM,2008:291-298.
[5] Zajic D, Dorr B J, Lin J. Single-document and Multi-document Summarization Techniques for Email Threads Using Sentence Compression[J].Information Processing and Management,2008, 44(4):1600-1610.
[6] 彭泽映,俞晓明,许洪波,等.大规模短文本的不完全聚类[J].中文信息学报, 2011,25(1):54-59.(Peng Zeying, Yu Xiaoming, Xu Hongbo, et al. Incomplete Clustering for Large Scale Short Texts[J]. Journal of Chinese Information Processing, 2011,25(1):54-59.)
[7] Newman M E J. Power Laws, Pareto Distributions and Zipf's Law[J]. Contemporary Physics, 2005,46(5):323-351.
[8] 黄承慧,印鉴,侯昉.一种结合词项语义信息和TF-IDF方法的文本相似度量方法[J].计算机学报,2011,34(5):856-864. (Huang Chenghui, Yin Jian, Hou Fang. A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method[J].Chinese Journal of Computers,2011,34(5):856-864.)
[9] 郝秀兰,胡运发,申情.中文论坛内容监测的方法研究[J].中文信息学报, 2012,26(3):129- 136. (Hao Xiulan, Hu Yunfa, Shen Qing. Research on Content Monitoring on Chinese Web Forums[J]. Journal of Chinese Information Processing, 2012,26(3):129- 136.)
[10] 刘美玲,郑德权,赵铁军,等.动态多文档文摘模型[J].软件学报,2012,23(2):289-298. (Liu Meiling, Zheng Dequan, Zhao Tiejun, et al. Dynamic Multi-document Summarization Model[J]. Journal of Software, 2012,23(2):289-298.)
[11] 徐永东,王亚东,刘杨,等.多文档文摘中基于时间信息的句子排序策略研究[J].中文信息学报,2009,23(4):27-33. (Xu Yongdong, Wang Yadong, Liu Yang, et al. Research on Temporal Information Based Sentences Ordering in Multi-document Automatic Summarization[J].Journal of Chinese Information Processing, 2009,23(4):27-33.)
[12] Lin C Y. ROUGE:A Package for Automatic Evaluation of Summaries[C]. In: Proceedings of the ACL-04 Workshop.2004:74-81.
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