The rapid dissemination of misinformation through social media increased the importance of automated fact-checking. Furthermore, studies on what deep neural models pay attention to when making predictions have increased in recent years. While significant progress has been made in this field, it has not yet reached a level of reasoning comparable to human reasoning. To address these gaps, we propose a multi-task explainable neural model for misinformation detection. Specifically, this work formulates an explanation generation process of the model's veracity prediction as a text summarization problem. Additionally, the performance of the proposed model is discussed on publicly available datasets and the findings are evaluated with related studies.

翻译：社交媒体上虚假信息的快速传播凸显了自动化事实核查的重要性。此外，近年来关于深度神经模型在做出预测时关注点的研究有所增加。尽管该领域已取得显著进展，但尚未达到与人类推理相媲美的逻辑推理水平。为解决这些不足，我们提出了一种用于虚假信息检测的多任务可解释神经模型。具体而言，本研究将模型可信度预测的解释生成过程构建为文本摘要问题。此外，本文在公开数据集上讨论了所提模型的性能，并基于相关研究对实验结果进行了评估。