Exploring Text Summarization for Fake News Detection

Jie Bian, Seungeon Lee, Karandeep Singh, Meeyoung Cha

http://doi.org/10.5626/JOK.2022.49.11.926

Fake news detection models need to gather and ingest massive information from heterogeneous sources rapidly for solid verification. This paper demonstrates the feasibility of applying text summarization, to uncover useful information or evidence for fake news detection. Two popular deep learning-based summarization techniques, extractive and abstractive, were used to generate condensed textual information from lengthy news content. Experiments on popular rumor debunking datasets show that two lines of summarized text can extract critical information, while improving the classification performance and substantially reducing inference time. Text summarization can also bring explainability by providing evidence from three levels: words, sentences, and documents.