An Integrated Cycle GAN and PEGASUS to Generate Synthetic Data for Detection of Fake News

An Integrated Cycle GAN and PEGASUS to Generate Synthetic Data for Detection of Fake News
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© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : T V Divya, Barnali Gupta Banik
DOI : 10.14445/22315381/IJETT-V70I8P206

How to Cite?

T V Divya, Barnali Gupta Banik, "An Integrated Cycle GAN and PEGASUS to Generate Synthetic Data for Detection of Fake News," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 57-70, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P206

Abstract
The proposed research to increase the size of the dataset with images, the regular image manipulation, which involves an image translation process using different graphical operations, makes the entire workflow design expensive. This process also involves designing complicated structures, which takes a lot of time to build the complete architecture. This image translation process has a major drawback, which involves controlled modification of the images. So, the proposed system implemented an Enhanced Cycle GAN, which does not perform any pairing activity between the images in the dataset. Since the detection of fake news involves both images and texts, the proposed research extracts the text using an ensemble mechanism in which the text summarizer is constructed using the pre-trained model known as “PEGASUS” combined LSTM to perform abstractive text summarization. After framing the summarization based on the context, the system is evaluated to prove its efficiency using ROUGE-N metrics.

Keywords
PEGASUS, pre-trained model, ROGUE-N, Abstractive Summarization, Cycle GAN, Recurrent NN models, Gradient Recurrent, Unit (GRU), Optimizer.

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