Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

Multi-Classification and Automatic Text Summarization of Kannada News Articles

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

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

	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 = {},
	doi = {10.5120/ijca2019918378},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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


  1. Keyword extraction based summarization of categorized Kannada text documents, Jayashree, R and Murthy, Srikanta K and Sunny, K, International Journal on Soft Computing, Vol 2, Number 4, Pg- 81, Year, 2011
  2. A context based text summarization system, Ferreira, Rafael and Freitas, Frederico and de Souza Cabral, Luciano and Lins, Rafael Dueire and Lima, Rinaldo and França, Gabriel and Simske, Steven J and Favaro, Luciano, 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pg- 66-70, 2014
  3. Automatic text summarization using fuzzy inference, Jafari, Mehdi and Wang, Jing and Qin, Yongrui and Gheisari, Mehdi and Shahabi, Amir Shahab and Tao, Xiaohui, Automation and Computing (ICAC), 2016 22nd International Conference on,pg- 256-260,2016
  4. Text summarization using Clustering technique, Deshpande, Anjali R and Lobo, LMRJ, International Journal of Engineering Trends and Technology
  5. ROCK: A robust clustering algorithm for categorical attributes,Guha, Sudipto and Rastogi, Rajeev and Shim, Kyuseok, Data Engineering, 1999. Proceedings., 15th International Conference on
  6. ] Document clustering: TF-IDF approach, Bafna, Prafulla and Pramod, Dhanya and Vaidya, Anagha, Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on, Pg-61-66,2016
  7. Automatic text summarization using a machine learning approach, Neto, Joel Larocca and Freitas, Alex A and Kaestner, Celso AA, Brazilian Symposium on Artificial Intelligence
  8. An Improvised Extractive Approach to Hindi Text Summarization, Kumar, K Vimal and Yadav, Divakar, Information Systems Design and Intelligent Applications
  9. Multi-Level based Stemming Based on Rules of Morphology for the Purposes of Implementation of Word Embeddings on Kannada, Lalithnarayan C and Shylaja Sharath S S
  10. Kannada stemmer and its effect on Kannada documents classification, Deepamala, N and Kumar, P Ramakanth, Computational Intelligence in Data Mining-Volume 3


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