Applying Automatic Text Summarization for Fake News Detection
- URL: http://arxiv.org/abs/2204.01841v1
- Date: Mon, 4 Apr 2022 21:00:55 GMT
- Title: Applying Automatic Text Summarization for Fake News Detection
- Authors: Philipp Hartl, Udo Kruschwitz
- Abstract summary: The distribution of fake news is not a new but a rapidly growing problem.
We present an approach to the problem that combines the power of transformer-based language models.
Our framework, CMTR-BERT, combines multiple text representations and enables the incorporation of contextual information.
- Score: 4.2177790395417745
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The distribution of fake news is not a new but a rapidly growing problem. The
shift to news consumption via social media has been one of the drivers for the
spread of misleading and deliberately wrong information, as in addition to it
of easy use there is rarely any veracity monitoring. Due to the harmful effects
of such fake news on society, the detection of these has become increasingly
important. We present an approach to the problem that combines the power of
transformer-based language models while simultaneously addressing one of their
inherent problems. Our framework, CMTR-BERT, combines multiple text
representations, with the goal of circumventing sequential limits and related
loss of information the underlying transformer architecture typically suffers
from. Additionally, it enables the incorporation of contextual information.
Extensive experiments on two very different, publicly available datasets
demonstrates that our approach is able to set new state-of-the-art performance
benchmarks. Apart from the benefit of using automatic text summarization
techniques we also find that the incorporation of contextual information
contributes to performance gains.
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