## 2 A Comprehensive Overview of the Literature
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Article** & **Dataset** & **Language** & **Features** & **Learning Algorithms and/or Tools** & **Results/Key Takeaways** \\ \hline \multirow{4}{*}{[8]} & \multirow{4}{*}{CEFR-graded dataset focusing on L2} & & Shallow, lexical, POS, syntactic, language modeling, entity grid, entity density, parse tree syntactic, lexico-semantic, discourse features & \multirow{4}{*}{Support Vector Machine (SVM)} & \multirow{4}{*}{Accuracy of 0.803 and PCC of 0.924 for L2 data. Ranking models outperformed classification models for novel datasets.} \\ \cline{1-1} \cline{5-5}  & & & Shallow, lexical, POS, syntactic, language modeling, entity grid, entity density, parse tree syntactic, lexico-semantic, discourse features & \multirow{4}{*}{Support Vector Machine (SVM)} & \multirow{4}{*}{Accuracy of 0.875 and PCC of 0.924 for L2 data. Ranking models outperformed classification models for novel datasets.} \\ \cline{1-1} \cline{5-5}  & Simple Wikipedia (100K sentence pairs), WeekBit (3125 articles), Common Core Standards Corpus (168 texts) & English & 151 Features including shallow, lexical, POS, syntactic complexity, psycholinguistic, features from Celex lexical database & Pearson Correlation, Root Mean Square Error, 10-fold cross-validation, WEKA and WEKA document-level models. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. packages like LIBSVM and the WEKA machine learning toolkit [12] contribute to these great results. We included WEKA in our research as well. In [6] (written in 2014), the researchers highlighted the importance of text readability assessment in clinical applications and healthcare in general. In their paper, they focused on the topic of estimating text difficulty and identifying the most crucial features contributing to it, which is something we also attempt to do but for a different target audience. They also used the Wikipedia and Simple Wikipedia corpus datasets, which we utilize as well, and examined the performance of selected classifiers and their 16 proposed features. After feature extraction, it was clear that character and word counts were significantly higher in difficult texts, which indicated their significance for the task. Among the POS features, nouns were found to be more useful compared to others. Finally, vocabulary was considered generally insignificant. Their findings also support the conclusions of other reviewed papers and show that shallow features are quite effective. The authors also presented results from tests using several machine learning algorithms that made use of the above feature collection. Their experimental setup used 10-fold cross-validation to randomly split the entire dataset, which consisted of 11,800 examples. The algorithm that achieved the highest classification accuracy was Random Forest, with an accuracy of over 74%, followed by Decision Trees and Linear Regression. The least effective algorithms were K-Nearest Neighbors and Naive Bayes, with accuracies below 64%. The tests showed that shallow and part-of-speech features yielded the best results for this kind of dataset and were on par with, or even better than, features from other groups. In a 2024 paper, the authors of [7] developed a novel multilingual model to assess the readability of Wikipedia articles, extending the evaluation beyond English to 14 languages. Their model demonstrated high accuracy in ranking readability in zero-shot scenarios, with accuracy usually over 80%. An interesting aspect of this paper is that the authors used both a text-based model (TRank) and a sentence-based model (SRank). Their solution is built upon a Neural Pairwise Ranking Model (NPRM) that uses a multilingual Masked Language Model (MLM), similar to mBERT. The model is trained with a Siamese network architecture, and then Margin Ranking Loss is applied.