Multi-document summarization using CS-ABC optimization algorithm

被引:0
|
作者
Kumar K.C. [1 ,2 ]
Nagalla S. [3 ]
机构
[1] Faculty of Computer Science, Kakinada Institute of Engineering and Technology
来源
Kumar, K. Chandra (chandrakumark2381@gmail.com) | 1600年 / European Alliance for Innovation卷 / 07期
关键词
Aggregate cross sentence frequency; Artificial bee colony based cuckoo search optimization technique; Inverse sentence frequency; Support vector machine classifier; Term frequency;
D O I
10.4108/EAI.13-7-2018.163835
中图分类号
学科分类号
摘要
In revolve handle to the information excess, the dramatic boost up documents, on the WWW, show the way of the accessibility of various credentials through the equal subject with conception. Within a limited time, a hard to inquire a suitable a particular document associated to a specific topic to fulfils user's compound data conditions. Hence, we have followed an effective document summarization system applying SVM classifier strategy by this paper. For choosing optimal sentence sets, the proposed technique applies the hybrid ABC-CS optimization algorithm. Further, established on few relevant features, SVM classifier approach is applied in finding the summary by ranking each of the optimal sentences. The operational proposal of JAVA and the results were examined for the methodology is implemented. © 2020 K. Chandra Kumar et al.
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