# Neural Abstractive Text Summarization and Fake News Detection

arXiv: https://arxiv.org/abs/1904.00788 · HTML: https://mlsanity.com/paper/1904.00788/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions](https://mlsanity.com/paper/2310.03195/) (2023)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning](https://mlsanity.com/paper/2203.02124/) (2022)
- [Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering](https://mlsanity.com/paper/2203.01294/) (2022)
- [Adversarial and Safely Scaled Question Generation](https://mlsanity.com/paper/2210.09467/) (2022)

## Abstract (arXiv)

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