Text Summarization and Classification for Indian Language
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10.5120/ijca2021921471 |
Manasi Chouk and Neelam Phadnis. Text Summarization and Classification for Indian Language. International Journal of Computer Applications 183(15):1-5, July 2021. BibTeX
@article{10.5120/ijca2021921471,
author = {Manasi Chouk and Neelam Phadnis},
title = {Text Summarization and Classification for Indian Language},
journal = {International Journal of Computer Applications},
issue_date = {July 2021},
volume = {183},
number = {15},
month = {Jul},
year = {2021},
issn = {0975-8887},
pages = {1-5},
numpages = {5},
url = {http://www.ijcaonline.org/archives/volume183/number15/31999-2021921471},
doi = {10.5120/ijca2021921471},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Over the last few years, there have been significant advances in Text Summarization. Text Summarization can be implemented using two approaches; one is the NLP based approach and another is Deep Learning approach. Text Summarization is a demanding and fascinating field of NLP. It has become important because of the tremendous increase in information and data. Text Summarization is technique of creating a specific and relevant short abstract of text using different ways like books, news articles, research papers, tweets etc. Research is being done to summarize large text documents which are difficult to summarize manually. For English and other foreign languages various automated text summarization systems are available. However very few techniques are available for Indian language such as Marathi. In this paper, two extractive techniques are proposed to summarize large Marathi texts. This paper also performs classification on Marathi text using Marathi headlines dataset.
References
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Keywords
NLP, Extractive technique, TF-IDF, Text Rank, Marathi Language