Methods for Automatic Text Generation

Dipti Pawade*, Mansi Jain**, Gauri Sarode***
* Assistant Professor, Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai, India.
**-*** UG Scholar, Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai, India.
Periodicity:December - February'2017
DOI : https://doi.org/10.26634/jcom.4.4.13418

Abstract

In the world of automating tasks and reducing human effort, it is essential for a computer to be able to produce text like humans. This will enable us to let the computer work on insignificant tasks, such as create a summary for an advertisement or a product as well as generate a different outlook on most things like a sequel for a movie or a book. In this paper, the authors aim to review different approaches that have been implemented for generating text based output and devise the most optimal approach that can be used. In this they have compared four different methods used to generate text from various scenarios.

Keywords

Automatic Text Generation, Natural Language Processing, Recurrent Neural Network, Long Short-Term Memory

How to Cite this Article?

Pawade,D., Jain,M., and Sarode,G. (2017). Methods for Automatic Text Generation. i-manager’s Journal on Computer Science, 4(4), 32-36. https://doi.org/10.26634/jcom.4.4.13418

References

[1]. Chandra Khatri, Sumanvoleti, Sathish Veeraraghavan, Nish Parikh, Atiq Islam, Shifa Mahmood, Neeraj Garg, and Vivek Singh, (2015). “Algorithmic Content Generation for Products”. Proceedings of IEEE International Conference on Big Data, Santa Clara, CA, pp.2945-2947.
[2]. Ilya Sutskever, James Martens, and Geoffrey Hinton, (2011). “Generating Text with Recurrent Neural Networks”. Proceedings of the 28 International Conference on Machine Learning, Bellevue, WA, USA, pp.1017-1024.
[3]. S. Thomaidou, I. Lourentzou, P. Katsivelis-Perakis, and M. Vazirgiannis, (2013). “Automated Snippet Generation for Online Advertising”. Proceedings of ACM International C o n f e re n c e o n I n f o rma t i o n a n d K n o w l e d g e Management (CIKM'13), San Francisco, USA, pp.1841- 1844.
[4]. Martens, J., (2010). “Deep learning via Hessian-free optimization”. Proceedings of 27 International Conference on MachineLearning (ICML), pp.735-742.
[5]. Singh, Manjeet, and Karunesh Kumar, (2014). “Concept based automatic ontology generation from domain specific text”. Soft Computing Techniques for Engineering and Technology (ICSCTET), International Conference on IEEE, Bhimtal, pp.1-5.
[6]. Neural Networks and Deep Learning. Retrieved from: http://neuralnetworksanddeeplearning.com/
[7]. Dong Wang, Chao Liu, Zhiyuan Tang, Zhiyong Zhang, and Mengyuan Zhao, (2015). “Recurrent Neural Network Training with Dark Knowledge Transfer”. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, IEEE, pp.5900-5904.
[8]. Andrej Karpathy, Justin Johnson, and Li Fei-Fei, (2015). “ Visualizing and Understanding Recurrent Networks”. arXiv preprint arXiv:1506.02078.
[9]. Hasim Sak, Andrew Senior, and Françoise Beaufays, (2014). “Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling”. In Fifteenth Annual Conference of the International Speech Communication Association.
[10]. D. Pawade, K. Rathod, S. Sethia, K. Dedhia, and H. Sonkamble, (2016). “Product Review Analysis Tool”. International Journal on Recent and Innovation Trends in Computing and Communication, Vol.4, No.4, pp.960- 963.
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