# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 6 Discussions  
This research attempts to answer the question: can the Arabic automatic review summarization improve by considering the reviews' sentiment factor? The result confirms that creating summaries for each polarity category yields better scores than the traditional summarization method. TextRank extractive summarization technique creates a summary using the PageRank approach, selects the most important sentences, and includes them in the final output. Using TextRank with the five-class rating grouping achieved the best score and the best average score, with _ROUGE_-1 of 0.6294 and 0.5256, respectively. However, the three-class rating grouping was the best on average. The rating grouping, in general, generated most of the high scores compared to the Mazajak API [37] and the sentiment-based approach (deep learning). However, in a few cases, the API grouping gave the best scores; in one case, the sentiment grouping yielded the best score, as seen in Tables 9 and 10. Human summary 1 generated better results, as the method used by this reviewer was selecting the sentences rather than rewriting new text; in contrast, reviewer 2 read all the reviews and rewrote the sentences in her writing style.  
The dataset contains reviews of more than a thousand hotels, with some having more than 5000 reviews. We selected two hotels with the median number of reviews (224). The chosen summarization ratio was 5% and 10%, and the rationale was to keep the automatic summary length close to the human summary. We tested other higher ratios, up to 30%, and those chosen yielded the best results and provided better readability. The fact that 10% of the reviews were adequate to generate these high scores is encouraging and indicates that the proposed approach has potential for future applications.  
To evaluate the summaries, we adopted the _BLEU_ and _ROUGE_ metrics. Although these metrics are widely used for summarization tasks, evaluating summaries needs some kind of human judgment, as done in [3; 34]. Moreover, when we analyzed how these algorithms work, we found that they have some limitations, as discussed in Section 4.5. Stemming, however, mitigated the shortcomings of these evaluation metrics, as shown in Table 5.  
Regarding Arabic review summarization, we only found one study in this area [34], where the study used hotel reviews from Tripadvisor and applied feature-base and sentiment-based summaries and evaluated the summaries subjectively, claiming 72% accuracy. Other Arabic studies applied graph-based methods for document summarization, such as [7] achieved an F-measure of 0.6799, [11] with an F-measure of 0.75, and [2] scored a _ROUGE_-1 value of 0.561-0.660 on 45% summary size. Summarizing reviews, however, is more challenging than documents, as reviews are noisy, redundant, and contain many spelling and grammatical errors. Despite these hurdles, our approach achieved an average _ROUGE_-1 score of 0.5256 on a 10% summary size.  
Finally, reviews are vital for businesses and e-commerce; summarizing reviews could be helpful to stakeholders and customers alike. Websites selling products or services strive to provide summary statistics for their reviews; adding text summaries is a compelling feature that would add a competitive advantage to the business.