# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Data Perspective
Table 2 offers a comprehensive categorization of the datasets used in fact verification systems, highlighting a variety of dataset types, each with distinctive attributes and challenges. The datasets are categorized meticulously based on their inherent nature and source, such as 'Knowledge-free Systems', 'Knowledge-Base-Based', 'Wikipedia-Based', 'Domain (Single)-Specific Corpus', and 'Mixed-domain Corpus (non-Wikipedia-based)'. Each type is represented with illustrative studies and remarks to provide insight into the inherent limitations or challenges in relation to enhancing explainability in AFV systems. The categorization is enriched with sub classifications under 'Knowledge Type', 'Text Type', and 'Domain Type'. 'Knowledge-free systems' are denoted with dashes (-) under 'Text Type' and 'Domain Type', indicating the inherent absence of these attributes. This underscores the retrieval-free nature of such systems, which predominantly rely on the intrinsic linguistic features of the claims, thus lacking contextual understanding. The 'Knowledge-Base-Based' type can be either single domain or multi domain, represented by check marks in both sub-categories under 'Domain Type'. This illustrates the versatility of knowledge-based systems in utilizing structured information from a specialized domain or amalgamating insights from multiple domains. The ability to cater to varied domains accentuates the expansive applicability of such systems, though it also brings forth challenges related to scalability and capturing nuanced information. 'Wikipedia-Based' datasets, inherently multi-domain, are highlighted separately to focus on the specific challenges of using Wikipedia as the main information source, such as dealing with potential biases and inaccuracies. The 'Domain (Single)-Specific Corpus is distinguished as it focuses on a specialized or singular domain, providing depth and specificity. While this focus allows for a detailed exploration of a particular domain, it also poses limitations due to the restricted scope and potential biases inherent to the selected domain, thereby affecting the overall evaluation and applicability of the system. Additionally, the 'Mixed-domain Corpus' type emphasizes the inclusion of diverse domains, especially those not solely reliant on Wikipedia, addressing the challenges arising from data heterogeneity and reliability. The categorization in Table 2, coupled with associated remarks, is intended to act as a resource, providing information on the various challenges and possibilities to improve explainability within AFV systems. This categorization can guide researchers and practitioners in making informed decisions regarding dataset selection and utilization, providing a clearer understanding of the implications and limitations of different dataset types in the context of Automated Fact Verification. This study acknowledges the extensive investigations conducted by [14] in explainable NLP and by [7] in explainable AFV, which provide meticulous lists and insightful analyses of prevalent datasets in their respective fields. It is crucial to clarify that the aim of this section (Section 3.3) is not to perform an exhaustive review of datasets, a task diligently undertaken by the aforementioned studies. Instead, it is uniquely positioned to illuminate the distinctive attributes and inherent diversity within various dataset types in AFV. This attempt to examine the impact of different data types on explainability serves as a thoughtful addition to ongoing discussions and reflections on the subject, offering a new perspective on the multifaceted interactions between data diversity and explainability in AFV.