Story scrambler - automatic text generation using word level RNN-LSTM

Story scrambler - automatic text generation using word level RNN-LSTM

Автор: Dipti Pawade, Avani Sakhapara, Mansi Jain, Neha Jain, Krushi Gada

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 6 Vol. 10, 2018 года.

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With the advent of artificial intelligence, the way technology can assist humans is completely revived. Ranging from finance and medicine to music, gaming, and various other domains, it has slowly become an intricate part of our lives. A neural network, a computer system modeled on the human brain, is one of the methods of implementing artificial intelligence. In this paper, we have implemented a recurrent neural network methodology based text generation system called Story Scrambler. Our system aims to generate a new story based on a series of inputted stories. For new story generation, we have considered two possibilities with respect to nature of inputted stories. Firstly, we have considered the stories with different storyline and characters. Secondly, we have worked with different volumes of the same stories where the storyline is in context with each other and characters are also similar. Results generated by the system are analyzed based on parameters like grammar correctness, linkage of events, interest level and uniqueness.

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Recurrent neural networks, Long short-term memory, Text generation, Deep learning

Короткий адрес: https://sciup.org/15016271

IDR: 15016271   |   DOI: 10.5815/ijitcs.2018.06.05

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