Abstract:
The volume of the online news has rapidly increased for recent years. People who consume
the news online have increased. New technologies have changed the way of consuming online
news. It has become a huge adoption of accessing news with the help of smart devices. When
people read online news, they scan the headlines first and then pay attention to the content.
Online Readers visit the articles in brief based on the headlines. Spotting fake news from
factual news is a challenging task. This paper presents a method to separate fake news from
factual news by comparing the similarities between the headlines and the content of the
articles using deep learning methods. In this proposed method, the abstractive text
summarization technique is used to extract the summary and then generate new headlines
for the articles. The selected dataset has 60 000 news articles and collected from a benchmark
dataset, BBC news. This model performs the best effort and achieves the accuracy 74% for
selected samples.
