论文标题
应用自动文本摘要以进行虚假新闻检测
Applying Automatic Text Summarization for Fake News Detection
论文作者
论文摘要
假新闻的分布不是一个新的，而是一个快速增长的问题。通过社交媒体向新闻消费的转变一直是传播误导性和故意错误信息的驱动力之一，因为除了容易使用之外，很少有任何真实性的监控。由于这种假新闻对社会的有害影响，对这些新闻的发现变得越来越重要。我们提出了一种解决问题的方法，该方法结合了基于变压器的语言模型的力量，同时解决了它们固有的问题之一。我们的框架CMTR-BERT将多个文本表示结合在一起，目的是规避顺序限制和相关信息的相关信息，而基础变压器架构通常会遭受。此外，它可以合并上下文信息。对两个非常不同的公开数据集进行的广泛实验表明，我们的方法能够为新的最先进的性能基准设置。除了使用自动文本摘要技术的好处外，我们还发现，上下文信息的合并有助于性能提高。
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.
