Neural Abstractive Text Summarization and Fake News Detection
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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
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1.
5 papers.
- 2023Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directionscited by 8
- 2021Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Mediacited by 5
- 2022Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learningcited by 2
- 2022Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clusteringcited by 0
- 2022Adversarial and Safely Scaled Question Generationcited by 0
- 2023Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions4 Oct 2023
- 2022Adversarial and Safely Scaled Question Generation17 Oct 2022
- 2022Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning4 Mar 2022
- 2022Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering2 Mar 2022
- 2021Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media21 May 2021