# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 3 Related Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Dataset** & **Language** & **Method** & **Evaluation** & **Results** & **Limitations** \\ \hline \multirow{4}{*}{[11]} & 25 Arabic articles & \multirow{4}{*}{Arabic} & Graph-based method, & \multirow{4}{*}{Precision: 0.79} & Limited dataset, \\  & gathered from & & PageRank, and MMR & & & & \\  & different websites on & & to filter redundant & & & \\  & different subjects & & sentences & & & \\ \hline \multirow{4}{*}{[2]} & Wikipedia pages and & \multirow{4}{*}{Arabic} & Deep learning, & \multirow{4}{*}{_ROUGE_-1: 0.561–0.660 on 45\% summary size} & Limited dataset, \\  & articles from some & & & & & \\  & popular Arabic & & & & & \\  & newspapers. & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{[34]} & Hotel reviews from & \multirow{4}{*}{Arabic} & Feature-base and & \multirow{4}{*}{_ROUGE_-1: 0.561–0. Many attempts have been made in text summarization in Arabic and English. Many methods were applied, and different document types, including web pages, Wikipedia pages, and reviews. Many Arabic datasets for document summarization research were found in the reviewed literature, such as EASC [35] and ANT [16]; also, [PERSON] et al. [5] stated in their comprehensive review the availability of 13 Arabic news datasets created for document summarization. Yet, no Arabic dataset was found that targets Arabic review summarization research. Moreover, studies focusing on Arabic review summarization are  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Dataset** & **Language** & **Method** & **Evaluation** & **Results** & **Limitations** \\ \hline \multirow{4}{*}{[7]} & \multirow{4}{*}{EASC dataset (153 Arabic articles) [35]} & \multirow{4}{*}{Arabic} & PageRank with the number of nouns in the sentence as starting value for each node & Five human summaries for each document, 0.6875 & Precision: 0.7294 & Document, focusing on clean news \\  & & & & & Recall: 0.7294 & articles, not \\  & & & & & F-measure: & replicable with \\  & & & & & 0.6799 & noisy reviews \\  & & & & & & or tweets \\ \hline \multirow{4}{*}{[33]} & \multirow{4}{*}{EASC [35] and ANT [16]} & \multirow{4}{*}{Arabic} & Analogical reasoning, an AI approach for learning by analogy & Human-generated summaries & _ROUGE_-1: 0.75 & \begin{tabular}{c} Single document, \\ focusing on \\ news, depends \\ on keywords \\ \end{tabular}  \\ \hline \multirow{4}{*}{[19]} & \multirow{4}{*}{Hotel reviews from the Tripadvisor website} & \multirow{4}{*}{English} & Two methods: & Method 1: & \\  & & 1. Selecting relevant sentences for the summary base on (TF-IDF) score & Human-generated summaries & 0.2101 _BLEU_: 0.7820 & The polarity-based \\  & & & 2. Pairing adjective to the nearest noun and considering the polarity & & Method 2: & showed poor \\  & & & & & 0.0670 _BLEU_: & performance \\ \hline \multirow{4}{*}{[3]} & \multirow{4}{*}{Restaurant reviews from the Tripadvisor website} & \multirow{4}{*}{English} & \multirow{4}{*}{ \begin{tabular}{c} Topic-based and sentiment-based summary using textRank \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Subjective evaluation, the summaries \\ \end{tabular}  } & \multirow{4}{*}{\ \begin{tabular}{c} Learning to \\ summaries \\ provided \\ sufficient and \\ precise \\ information \\ about the target \\ restaurant \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Using \\ subjective evaluation only \\ \end{tabular}  } \\  & & & & &  
limited; only one study was found in this area [34]. Thus, this research investigates a graph-based approach, TextRank, in the review summarization task. Graph-based summarization methods are language-independent; therefore, no linguistic resources are needed, which are scarce in the Arabic language [11]. Additionally, we propose to combine TextRank with sentiment analysis to improve overall performance and readability. The dataset consists of hotel reviews collected for sentiment analysis purposes [36].