## 3 Dataset
### Dataset Description  
The dataset includes 589 unique claims. As shown in Figure 1, the majority of these claims are supported by 6 pieces of evidence (approximately 70%), while a smaller subset of claims has only 1 piece of evidence (about 22%). A minor portion of claims is associated with 12 pieces of evidence (around 7%). This range of evidence distribution provides flexibility in terms of the depth and complexity of explainability within the fact-verification process. \begin{table}
\begin{tabular}{c c} \hline \hline
**Header** & **Description** \\ \hline \multirow{2}{*}{_Claim\_Topic\_ID_} & A unique identifier representing the claim, which also encodes information about the thematic topic it belongs to. For example, in the identifier Claims\_Climate\_B2\_0\_1, the middle segment (‘Climate’) denotes the theme, while B2.0 refers to the annotation team responsible for curating the claim, facilitating efficient data processing. \\ \hline \multirow{2}{*}{_Claim\_Text_} & The textual content of the claim, which needs to be verified for its truthfulness. \\ \hline \multirow{4}{*}{_Evidence\_Topic\_ID_} & A unique identifier for the evidence corresponding to each claim. The Evidence ID is constructed by \\  & consolidating the claim ID with the prefix ‘Evidence’ and appending a unique serial number. For example, in \\  & Evidence\_Claims\_Climate\_B2\_0\_1\_n, the ‘n’ uniquely distinguishes each piece of evidence associated with the \\  & claim. This structure efficiently organizes the multiple pieces of evidence for a given claim. \\ \hline \multirow{2}{*}{_Evidence\_Text_} & The actual textual evidence supporting or refuting the claim. \\ \hline \multirow{2}{*}{_Label_} & The label indicating the veracity of the claim (T/F/N representing True/False/Not Enough Info, respectively). \\ \hline \multirow{2}{*}{_Reason_} & An explanation that provides justification for the label assigned to the claim. \\ \hline \multirow{2}{*}{_Reason\_Type_} & Classifies the nature of the explanation as either Abstractive for human-generated explanations that are crafted \\  & based on the evidence but not directly copied, or Extractive, copied directly from the supporting evidence. If \\  & no explanation is provided, it is marked as Nil. \\ \hline \multirow{2}{*}{_Annotation\_ID_} & An identifier assigned to each entry, reflecting the annotation team responsible for curating the data. There are \\  & three types of IDs, \(B\_2\_0\), \(C\_2\_1\), \(C\_2\_2\), corresponding to the three annotation teams involved, allowing for \\  & traceability back to the raw files from each team during data processing and analysis. \\ \hline \multirow{2}{*}{_Article\_Topic\_ID_} & A reference to the specific source or article from which the evidence is derived. This ensures the data can be \\  & linked back to the original source used by the teams during the annotation process. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key Components of FactVer. Furthermore, Figure 2 illustrates the distribution of text length for Claim_Text, Evidence_Text and Reason. The following key points can be observed:  
* Claim_Text Length: The majority of claims are concise, typically within the 40-60 word range. Longer claims exceeding 100 words are less common. * Evidence_Text Length: Evidence text length varies widely, with most evidence ranging from 150 to 250 words, though some extend up to 700 words, reflecting varying levels of detail required to support different claims. * Reason Length: Reasons are generally concise, with most falling within the 50-100 word range. While some explanations exceed 200 words, very few extend beyond 400 words. This distribution highlights the diverse nature of the dataset, where claims, evidence texts, and human-generated reasons exhibit varying lengths, potentially reflecting the differing complexity of fact-verification instances. Figure 1: Distribution of the amount of evidence associated with each claim across all themes.