Neural Abstractive Text Summarization and Fake News Detection
This work is incremental, applying existing methods to text summarization and a related fake news detection task without novel breakthroughs.
The paper tackles abstractive text summarization by comparing models like LSTM-encoder-decoder, pointer-generator networks, and transformers, and extends this to fake news detection by using summarization as a feature extractor, but no concrete results or numbers are provided.
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