# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification  
[PERSON]  
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia  
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[PERSON]  
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia  
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[PERSON]  
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the Arabic language due to its rich morphological structure, the range of dialects, and the scarcity of data and tools [5]. Arabic is the fifth most spoken language globally, with more than 400 million speakers worldwide [6]. The summarization of Arabic text still suffers from low performance and fewer research studies [7]. Moreover, most Arabic ATS targeted document summarization and very few focused on customer reviews. ATS can be classified according to the number of simultaneously processed single- and multi-document documents [5]. Single-document summarization is an automatic procedure focusing on extracting useful information from a single text, such as an article, research, or webpage [8]. In contrast, multi-document summarization is an automated procedure that focuses on extracting critical information from multiple texts sharing the same topic, such as various types of research about the same issue [9]. Reviews can be viewed as multiple documents; however, to summarize reviews using TextRank, they should be combined as a single document, and then one final summary is produced. Reviews usually include positive, negative, and neutral sentiments [3]; we hypothesize that involving sentiment in the summarization process could enhance the performance of the ATS system and improve the readability of the generated summary. This study proposes an approach for summarizing Arabic hotel reviews based on review polarity. TextRank extractive text summarization was applied in conjunction with sentiment analysis. Arabic natural language processing is an immature research area. Although there is a growing interest in the area, there is still a lack of specialized tools and lexicons. TextRank was chosen for this work for several reasons. First, it is language-independent. Second, the graph-based nature of this algorithm, that is, selecting a whole sentence instead of parts of the sentence, makes it a suitable option for reviews. This research does not focus on the summarization method per se; it focuses on the effect of combining the sentiment with the summarization. A comparison between the different summarization methods can be found in [9; 10; 11; 12]. The proposed method comprises two main stages: labeling the reviews with the sentiment using supervised and unsupervised techniques; then summarizing the labeled reviews. Two human summaries were authored to evaluate the proposed ATS system. The _BLEU_ and _ROUGE_ scoring techniques were used to assess the performance of the proposed system. Thus, the research question of this study can be phrased as follows:  
* Can the Arabic automatic review summarization improve by considering the reviews' sentiment factor? The rest of this study is organized as follows: Section 2 reviews the literature, and three discusses some related works. The proposed methodology is presented in Section 4.