BHLM:Bayesian theory-based hybrid learning model for multi-document summarization

作     者：S.Suneetha A.Venugopal Reddy 

作者机构：Computer Science and Engineering Department Hasvita Institute of Engineering and Technology HyderabadIndia Jawaharlal Nehru Technological University HyderabadIndia 

出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)

年 卷 期：2018年第9卷第2期

页      面：229-250页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：Multi-document text feature sentence score hybrid learning model Bayesian theory 

摘      要：In order to understand and organize the document in an efficient way,the multidocument summarization becomes the prominent technique in the Internet *** the information available is in a large amount,it is necessary to summarize the document for obtaining the condensed *** perform the multi-document summarization,a new Bayesian theory-based Hybrid Learning Model(BHLM)is proposed in this ***,the input documents are preprocessed,where the stop words are removed from the ***,the feature of the sentence is extracted to determine the sentence score for summarizing the *** extracted feature is then fed into the hybrid learning model for ***,learning feature,training error and correlation coefficient are integrated with the Bayesian model to develop ***,the proposed method is used to assign the class label assisted by the mean,variance and probability ***,based on the class label,the sentences are sorted out to generate the final summary of the *** experimental results are validated in MATLAB,and the performance is analyzed using the metrics,precision,recall,F-measure and *** proposed model attains 99.6%precision and 75%rouge-1 measure,which shows that the model can provide the final summary efficiently.