Explaining Veracity Predictions with Evidence Summarization: A
  Multi-Task Model Approach
Recep Firat Cekinel Pinar Karagoz
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.