Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We used the AraBERT model in two different variants. First, as a feature extractor in addition to the text vectorization schemes' features. We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units. Second, AraBERT is adopted as a pre-trained model, and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences. To evaluate the research results, we conducted several experiments to compare the use of the AraBERT model in its two variants. In terms of Mean Absolute Percentage Error (MAPE), the results show minor variance between AraBERT v0.2 as a feature extractor (21.7723) and the fine-tuned AraBERT v2 (21.8211). On the other hand, AraBERT v0.2-Large as a feature extractor outperforms the finetuned AraBERT v2 model on the used data set in terms of the coefficient of determination (R 2 ) values (0.014050,−0.032861), respectively.