# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 7 Conclusions and Future Work  
This research provides a novel automatic text summarization framework for Arabic online reviews based on polarity. We used the Arabic hotel reviews dataset and TextRank graph-based summarization technique. Obtaining the sentiment for reviews, whether by considering the rating score or text mining, yielded better summarization performance scores than the baseline approach. The best average _BLEU_ score achieved was 0.5047 using the three-class grouping, while the best average _ROUGE_-1 score was 0.5256 using the five-class rating grouping. Both scores outperform TextRank, which reached a _BLEU_ score of 0.3507 and a _ROUGE_-1 score of 0.4914. Although many Arabic studies investigated document summarization techniques, only one study was found that targeted Arabic reviews; furthermore, no Arabic dataset was available for review summarization research.  
The evaluation was conducted by selecting two hotels with the median number of reviews. Human summaries were authored by two native Arabic speakers for the two selectedhotels. Although more hotels with a different number of reviews should be considered to achieve better generalization, this work can be viewed as a seed for further research and development in Arabic reviews ATS. Additionally, applying other graph-based methods, such as LexRank, was challenging due to the lack of Arabic summarization tools.  
The obstacles faced in this study opened many future research opportunities. There is a need for more Arabic open-source tools and Arabic review datasets for various products and services, including restaurants, hotels, books, and mobile apps. Review datasets should also include human summaries, preferably by more than one native Arabic speaker. Nevertheless, other qualitative methods should be considered for evaluating summaries, such as those suggested in [3; 35]. Different approaches, including deep learning, could be investigated and compared with graph-based methods. Additionally, more focus is needed on multi-document summarization, as few studies have been conducted in this area, especially for the Arabic language. Additionally, review summarization could benefit from adding an aspect-based categorization [3]. Finally, combining text summarization with more text preprocessing, such as correcting spelling and eliminating duplicates, would improve the performance and summary readability. Although there is a scarcity of Arabic textual resources, tools, and datasets, we were able to build an Arabic sentiment-based review summarizer with remarkable results. We hope that the positive results achieved by this work encourage more research in the highlighted areas to enrich Arabic research and development.  
Conceptualization, A.A., G.A. and H.S.A.; methodology, G.A. and H.S.A.; software, H.S.A.; validation, A.A., H.S.A. and G.A., and formal analysis, H.S.A.; resources, G.A. and H.S.A.; data curation, H.S.A.; writing--original draft preparation, A.A., G.A. and H.S.A.; writing--review and editing, G.A. and H.S.A.; visualization, H.S.A. and G.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The dataset used is publicly available and was obtained from [36].  
The authors declare no conflict of interest.