## 2 A Comprehensive Overview of the Literature  
Most papers in the relevant literature study reader target groups and readability assessment on entire corpora or texts. The novelty of this work is twofold: first, we focus on sentences, which are used as building blocks for paragraphs or whole texts/essays; and, secondly, we target a much broader audience. By reviewing the existing literature, we aim to shed light on the key concepts, debates, and gaps in knowledge surrounding this subject in order to improve. This review serves as a foundation for our research and offers a synthesis of the diverse perspectives and methodologies that have shaped ours. We also provide a quick overview of the most important reviewed articles in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Dataset** & **Language** & **Features** &
\begin{tabular}{p{113.8 pt}} **Learning Algorithms** \\ **and/or Tools** \\ \end{tabular} & **Results/Key Takeaways** \\ \hline
[4] & \begin{tabular}{l} 15 news texts from \\ Reuters \\ \end{tabular} & English & \
\begin{tabular}{l} Shallow, cohesion, \\ syntactic, lexical \\ features, connectives \\ \end{tabular} & Coh-Metrix NLP tool &
\begin{tabular}{l} Long sentences, complex words, \\ and syntactic complexity \\ decrease readability. Pronoun \\ density confuses readers. \\ \end{tabular} \\ \hline
[5] & \begin{tabular}{l} 1433 articles from \\ Weekly Reader \\ magazine, \\ LocalNews2007, \\ LocalNews2008 \\ \end{tabular} & English & \begin{tabular}{l} Discourse, entity \\ density, lexical chain, \\ coreference interference, \\ entity grid features \\ \end{tabular} & \
\begin{tabular}{l} 10-fold cross-validation, \\ LIBSVM, WEKA \\ \end{tabular} &
\begin{tabular}{l} Entity-density features \\ sentence length is the most \\ efficient shallow feature. \\ Noun-based POS features have \\ strong predictive power. \\ \end{tabular} \\ \hline
[6] & \begin{tabular}{l} Wikipedia and Simple \\ Wikipedia corpus \\ (11,800 examples) \\ \end{tabular} & English & \begin{tabular}{l} 16 features including \\ surface level, POS, \\ vocabulary, concept \\ density, aggregate \\ features \\ \end{tabular} & \
\begin{tabular}{l} Random Forest, \\ Decision Trees, Linear \\ Regression, K-Nearest \\ Neighbors, Naive Bayes, \\ SVM \\ \end{tabular} &
\begin{tabular}{l} Random Forest achieved over \\ 74% accuracy. Shallow and POS \\ features yielded the best results. \\ \end{tabular} \\ \hline
[7] & \begin{tabular}{l} Wikipedia articles in 14 \\ languages \\ \end{tabular} & Multilingual & \begin{tabular}{l} Text-ranking and \\ sentence-ranking \\ models, number of \\ sentences, Flesch \\ reading ease, linguistic, \\ language-agnostic \\ features \\ \end{tabular} & \
\begin{tabular}{l} Neural Pairwise \\ Ranking Model \\ (NPRM), Multilingual \\ Masked Language \\ Model (MLM), Siamese \\ Network Architecture, \\ Margin Ranking Loss \\ \end{tabular} &
\begin{tabular}{l} TRank outperformed SRank and all baselines. [PERSON] reading ease performed well in English but not in other languages. \\ Sentence-based approach makes \\ classification harder. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of articles, datasets, languages, features, learning algorithms/tools, and results. In their 2013 article [4], [PERSON], [PERSON], and [PERSON] examined readability assessment features for L2 readers. The term Level 2 (L2) readers refers to those learning a second language; for example, immigrants, refugees, or tourists who are attempting to fit into a new environment and understand a new language. L2 learners have much to gain by acquiring reading material that aligns with their language proficiency, as previously discussed. The dataset in the aforementioned paper consisted of 15 news texts taken from the Reuters news agency website, each containing four to seven sentences.