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Neural Abstractive Text Summarization and Fake News Detection

Authors :
Esmaeilzadeh, Soheil
Peh, Gao Xian
Xu, Angela
Publication Year :
2019
Publisher :
arXiv, 2019.

Abstract

In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractive text summarization task. Finally, as an extension of our work, we apply our text summarization model as a feature extractor for a fake news detection task where the news articles prior to classification will be summarized and the results are compared against the classification using only the original news text. keywords: LSTM, encoder-deconder, abstractive text summarization, pointer-generator, coverage mechanism, transformers, fake news detection

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....673c5770aa3fa3ff349f3e19ebddd1bd
Full Text :
https://doi.org/10.48550/arxiv.1904.00788