## 2 A Comprehensive Overview of the Literature
To evaluate their model, they set baselines representative of the most common approaches, such as the number of sentences, Flesch reading ease, linguistic features, language-agnostic features, and a classification-based approach instead of a ranking-based one. After extensive tests, TRank significantly outperformed SRank as well as all the baselines. The authors also highlighted that Flesch reading ease performs well in English but not in other languages. Their results also highlighted that the sentence-based approach makes classification harder. On the matter of ranking compared to the classification approach, the authors of [8] developed a system using an SVM to assess text readability for both native speakers and L2 learners. They achieved an accuracy of 0.803, a Pearson Correlation Coefficient (PCC) of 0.900 for native data, and an accuracy of 0.785 with a PCC of 0.924 for L2 data. They compared the effectiveness of ranking and classification models, favoring ranking for its better performance with novel datasets. The study also explored methods like domain adaptation and self-training, which significantly improved accuracy in estimating Common European Framework of Reference (CEFR) levels and readability, especially when L2 data were limited. However, the focus on SVM models may overlook the potential of other machine learning algorithms. In [9; 13], the authors set a goal to construct a readability model that applies specifically to sentences instead of documents and that works well and offers portability across different genres of corpora. They also used the Simple Wikipedia dataset along with others while utilizing tools like Coh-Metrix. Specifically using their WeeBit-trained model on the OneStopEnglish corpus, they performed a classification of sentences that were manually aligned in order to classify them into three different groups; the accuracy of this model was 78%. They also used a dataset of 100 K sentence pairs from Wikipedia and Simple Wikipedia, from which they extracted 151 features and attempted binary classification. The best accuracy they achieved in this task, after multiple tests with different feature subsets and with varying training set sizes, was 66% using SMO as the classifier and 10-foldcross-validation. They also compared the performance of sentence-level models to that of document-level models and concluded that sentence-level models on the sentence task are significantly less effective than document-level models on the document task, which validates the complexity of this problem and our venture. Investigating the long-term applicability and scalability of such models would be beneficial. In a study published in the MDPI Analytics journal in 2023 [14], regarding the creation of readability models and features, the researchers aimed to develop a universal readability index applicable to different alphabetical languages, which takes into account readers' short-term memory processing capacity. The study observed that existing readability formulas did not consider the number of words between punctuation marks. A custom formula was proposed that could potentially provide a synthetic measure of human reading difficulty. This further supports the idea that studying the effectiveness of features and their nature could further hint at improvements and lead to the creation of new features, even based on older ones. On the other hand, regarding this specific formula, the focus on short-term memory processing capacity as a key factor might overshadow other important factors such as prior knowledge, motivation, and special reading strategies. Additionally, since there is a lack of empirical validation across diverse reading populations and languages, future iterations might consider testing this. Providing results and further testing the simplicity of use, as well as the accuracy across a wide dataset of different texts in various languages, is important, especially since this single formula claims to be applicable to any alphabetical language.