# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 6 Conclusions and Future Work  
This paper demonstrates an exploratory experiment in which we collected human reading data during the reading of Arabic texts, used eye-tracking features from the reading literature, and analyzed the results. The results show an improvement in the readability prediction task. The degree of this improvement varied based on the type of algorithm used, which indicates that a relationship exists between eye-tracking features and readability prediction. Further investigation of this observation is needed from different dimensions, such as the nature of the prediction algorithm and its associated parameters. Additionally, eye-movement measures were found to be superior to linguistic features when combined, which may indicate the ability of these features to reflect the readability level of the text in a more natural and precise way. Our results do have some limitations, such as the insufficiency of data and the number of features used and the ML models used not being state-of-the-art. No generalization could be obtained about this relationship and its strength. However, based on the behaviors of different regression models and the error decrease, we infer a relationship. This study motivates us to explore this subarea of readability assessment more deeply and examine the significance of using gaze data in Arabic readability assessment. For future work, because a very important success factor for readability studies is the availability of sufficiently large and graded gold-standard datasets, which is not the case for Arabic, we plan to build an improved Arabic human reading corpus similar to the widely used Dundee corpus [82]. Additionally, we plan to feature engineer eye-tracking data to understand their effects on readability assessment, particularly for Arabic. Offering an open-source Arabic human reading corpus will be an excellent contribution for Arabic \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Texts** & **FC** & **FDM** & **SAM** & **SC** & **RC** & **Participants’ Common Subjective Readability** \\ \hline Texts 1–3 & 85.76 & 283.37 & 1.73 & 85.97 & 47.05 & E \\ \hline Texts 4–6 & 103.6 & 286.69 & 1.64 & 103.83 & 53.59 & M/D \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average text features. that will open the door for broader utilization of eye-tracking for readability assessment of many NLP tasks, such as text simplification, machine translation, text summarization, etc. Additionally, regarding the ML model, NNs have shown significant advancement in NLP, where word embedding programs, such as word2 vec and GloVe, have enabled the accurate modeling of the semantic relationships between words. Using these relationships in readability assessments has shown encouraging results [37; 49]. Additionally, RNNs have been used widely in psycholinguistics and NLP research, particularly reading research. It will be worth using different RNN variations (simple recurrent networks (SRNs), long short-term memory networks (LSTMs), and GRUs) and observe how they could model human cognitive processing more accurately over traditional ML models such as SVM, naive Bayes, and decision trees. Thus, it is worth investigating these neural methods for Arabic readability prediction, particularly with eye-tracking features. Another alternative is transformers that could be fine-tuned to target the readability prediction task. Lastly, we aim to use different texts, such as news articles or textbooks, to observe how modeling human cognitive processing differs depending on the text used. Conceptualization, I.B., H.A.-K. and A.A.-S.; methodology, I.B., H.A.-K. and A.A.-S.; software, I.B. ; validation, I.B. and H.A.-K.; formal analysis, I.B. and H.A.-K.; investigation, I.B., H.A.-K. and A.A.-S.; resources, I.B. and H.A.-K.; data curation, I.B. ; writing--original draft preparation, I.B. ; writing--review and editing, H.A.-K. and A.A.-S.; visualization, I.B.