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Indian Language Text Summarization Using Recurrent Neural Networks

Anjali A V1 , N. Ramasubramanian2 , A. Santhanavijayan3

Section:Survey Paper, Product Type: Journal Paper
Volume-06 , Issue-11 , Page no. 162-164, Dec-2018

Online published on Dec 31, 2018

Copyright © Anjali A V, N. Ramasubramanian, A. Santhanavijayan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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How to Cite this Paper

IEEE Style Citation: Anjali A V, N. Ramasubramanian, A. Santhanavijayan, “Indian Language Text Summarization Using Recurrent Neural Networks,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.162-164, 2018.

MLA Style Citation: Anjali A V, N. Ramasubramanian, A. Santhanavijayan "Indian Language Text Summarization Using Recurrent Neural Networks." International Journal of Computer Sciences and Engineering 06.11 (2018): 162-164.

APA Style Citation: Anjali A V, N. Ramasubramanian, A. Santhanavijayan, (2018). Indian Language Text Summarization Using Recurrent Neural Networks. International Journal of Computer Sciences and Engineering, 06(11), 162-164.

BibTex Style Citation:
@article{V_2018,
author = {Anjali A V, N. Ramasubramanian, A. Santhanavijayan},
title = {Indian Language Text Summarization Using Recurrent Neural Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {06},
Issue = {11},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {162-164},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=563},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=563
TI - Indian Language Text Summarization Using Recurrent Neural Networks
T2 - International Journal of Computer Sciences and Engineering
AU - Anjali A V, N. Ramasubramanian, A. Santhanavijayan
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 162-164
IS - 11
VL - 06
SN - 2347-2693
ER -

           

Abstract

Text summarization is the process of creating short and accurate summary of a given text document. The paper is proposing an abstractive method of text summarization for text in Indian languages. The proposed algorithm uses an encoder-decoder recurrent neural networks with attention mechanism. The results observed were significantly better compared to the performance of already existing Indian language summarizer.

Key-Words / Index Term

Abstractive Summarization, LSTM, Recurrent Neural Network (RNN)

References

[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166, 2017.
[2] K. Cho, B. Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Arxiv preprint arXiv:1406.1078,2017.
[3] M. Johnson et al., “Google’s multilingual neural machine translation system: Enabling zero-shot translation,” Transactions of the Association for Computational Linguistics, vol. 5, no. 20, pp. 339–351, 2016.
[4] J. Lee, K. Cho, and T. Hofmann, “Fully character-level neural machine translation without explicit segmentation,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 365–378, 2017.