# Revisiting Text Guide, a Truncation Method for Long Text Classification
## 3 Results and Discussion
### The Added Benefit of Increased Interpretability  
In the final part of the discussion, we note an added benefit of utilizing the Text Guide: the increased interpretability of the whole classification process owed to the highly interpretable language model used to construct the sIFTL. When the naive truncation method is concerned, it is difficult to interpret which text fragments were precisely the focus of the later-used deep learning language model without any dedicated methods from \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Data Set**} \\ \hline  & & \multicolumn{3}{c}{**IMDB FULL**} & \multicolumn{3}{c}{**IMDB 510–100**} \\ \hline \multirow{2}{*}{**Model**} & **Truncation** & \multirow{2}{*}{**ROC AUC Score**} & **F1 Micro** & **MCC** & **ROC AUC Score** & **F1 Micro** & **MCC** \\ \hline \multirow{2}{*}{RoBERTa} & Naive & 0.9483 & 0.8767 & 0.7536 & 0.8617 & 0.7886 & 0.5738 \\ \cline{2-8}  & Text Guide & 0.9492 & 0.8787 & 0.7576 * & 0.8942 & 0.8132 & 0.6234 *** \\ \hline \multirow{2}{*}{BERT} & Naive & 0.9282 & 0.8493 & 0.6986 & 0.8652 & 0.7817 & 0.5595 \\ \cline{2-8}  & Text Guide & 0.9265 & 0.8472 & 0.6944 & 0.8545 & 0.7778 & 0.5516 \\ \hline \multirow{2}{*}{DistilBERT} & Naive & 0.924 & 0.8447 & 0.6894 & 0.8619 & 0.7869 & 0.5702 \\ \cline{2-8}  & Text Guide & 0.9237 & 0.8442 & 0.6884 & 0.8758 & 0.8033 & 0.6032 ** \\ \hline \multirow{2}{*}{SqueezeBERT} & Naive & 0.9343 & 0.8559 & 0.7119 & 0.8856 & 0.806 & 0.6089 \\ \cline{2-8}  & Text Guide & 0.9353 & 0.8579 & 0.7158 & 0.902 & 0.8204 & 0.638 * \\ \hline \hline \end{tabular}  
* * sign indicates a weakly significant difference, ** indicates a significant difference, ** denotes highly significant difference between model versions deployed on text instances truncated by Text Guide and in the naive manner.  
\end{table} 
Table 7: Experiments performed with selected pre-trained transformer models on IMDB data sets. For both data sets and all models, the adopted parameters for Text Guide were: XGBoost feature importance as the feature importance method, OLT = 1, all occurrences of important tokens were taken into consideration, the part of the beginning of the original text instance used by Text Guide = 0.1, the part of the ending of the original text instance used by Text Guide = 0, number of tokens surrounding the important token to be used for creating the pseudo sentence = 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Set** & **Text Guide Version** & **MCC** & **Gain Over Study [1]** & **XAI Method** &
\begin{tabular}{c} **Important Token** \\ **Occurrences** \\ \end{tabular} \\ \hline \multirow{2}{*}{DMOZ 510–1500} & From study [11] & 0.659 & \multirow{2}{*}{0.005} & XGBoost feature importance & \multirow{2}{*}{1} \\ \cline{2-5}  & Best from this study & & & XGBoost feature importance & \\ \hline \multirow{2}{*}{DMOZ 1500+} & From study [11] & 0.5913 & \multirow{2}{*}{0.0084} & XGBoost feature importance & \multirow{2}{*}{1} \\ \cline{2-5}  & Best from this study & & & Tree SHAP & All \\ \hline \multirow{2}{*}{DMOZ 4090+} & From study [11] & 0.5138 & - & XGBoost feature importance & \multirow{2}{*}{1} \\ \cline{2-5}  & Best from this study & 0.5256 & 0.0118 & XGBoost feature importance & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results achieved by the original Text Guide method [11] and best results from our study. The presented results were obtained by pre-trained AlBERT models. Only the varying parameters of the Text Guide were mentioned. the explainable AI domain. However, when the Text Guide is concerned, inspecting the obtained text instance allows us to explicitly observe which important tokens from the sIFTL were used to form the text instance of the desired length.