Document Summarization using A Hybrid Trace Thrash Data Modeling and Classification Algorithms

S.Dilliarasu*, R. Thirumalaiselvi**
* Research Scholar, Bharath University, Selaiyur, Chennai, India.
** Assistant Professor, Department of Computer Science, Govt. Arts College for Men (Autonomous), Nandanam, Chennai, India.
Periodicity:April - June'2015
DOI : https://doi.org/10.26634/jse.9.4.3531

Abstract

Multi-record rundown is utilized for comprehension and investigation of substantial record accumulations, the significant wellspring of these accumulations are news documents, sites, tweets, website pages, examination papers, web indexed lists and specialized reports accessible over the web and different spots. A few cases of the uses of the Multi-report synopsis are breaking down the web list items for helping clients in further skimming and creating outlines for news articles. Report preparing and synopsis era in an expansive content record gathering is computationally unpredictable errand and in the period of Big Data examination where size of information accumulations is high, there is need of calculations for condensing the huge content accumulations quickly. Here the authors display, a Trace Thrash, A multi archive summarizer is introduced in this work with the assistance of semantic likeness based grouping over the prevalent disseminated figuring system Trace Thrash.

Keywords

Trace Thrash, Huge Data, Data Modeling and Classification.

How to Cite this Article?

Arasu, S. D., and Thirumalaiselvi, R. (2015). Document Summarization using A Hybrid TraceThrash Data Modeling and Classification Algorithms. i-manager’s Journal on Software Engineering, 9(4), 25-31. https://doi.org/10.26634/jse.9.4.3531

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