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Multi-Classification and Automatic Text Summarization of Kannada News Articles

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
Anusha B. S. Harshitha P. Divya Ramesh, Uma D. Lalithnarayan C.
10.5120/ijca2019918378

Anusha Harshitha Divya B S P Ramesh and Uma Lalithnarayan D C.. Multi-Classification and Automatic Text Summarization of Kannada News Articles. International Journal of Computer Applications 181(38):24-29, January 2019. BibTeX

@article{10.5120/ijca2019918378,
	author = {Anusha B. S. Harshitha P. Divya Ramesh and Uma D. Lalithnarayan C.},
	title = {Multi-Classification and Automatic Text Summarization of Kannada News Articles},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2019},
	volume = {181},
	number = {38},
	month = {Jan},
	year = {2019},
	issn = {0975-8887},
	pages = {24-29},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume181/number38/30283-2019918378},
	doi = {10.5120/ijca2019918378},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Kannada is a historical language with abundant resources which display the tradition and culture of Karnataka. Extraction of most important and meaningful information from one or more large documents of text in the form of summary is a challenging task in regional languages compared to English. The main objective of present paper is to get the automatic summary of news articles from several sources. Naive-Bayes algorithm is used for classification of different categories of news articles include sports, politics and general. To find the sub-categories from each category such as state, national and international a Rock clustering algorithm has been used and the summary have been extracted automatically. Data is collected from multiple sources of summarization. A word vectorising stemmer approach is used to reduce the morphological complexity of the resources and a sub-sampling approach is used for efficient optimization and to reduce the complexity

References

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Keywords

Text Summarization, Under Resourced Language, ROCK Clustering, Naïve- Bayes, Classification