# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 3 Related Work  
Text summarization research has been around since 1958 [17], where the problem was approached considering the frequency of terms in a document. As tools and techniques developed, recent studies applied various methods, from word and phrase counts to deep learning architectures and graph-based methods. In [18], the authors proposed a multi-document summarization using TextRank, and to reduce redundancy in the output summary, they used Maximal Marginal Relevance (MMR). The evaluation metrics _ROUGE_-1 and _ROUGE_-2 were used, and the results achieved were F scores of 0.5103 and 0.4257, respectively. In [19], a system for summarizing hotel reviews collected from the Tripadvisor website is proposed. The method is based on textual features achieving a _ROUGE_-1 score of 0.2101 and a _BLEU_ score of 0.7820. Several studies investigated Arabic single-document summarization [20; 21; 22; 23; 24; 25] and multi-document summarization [23]. The approaches taken in prior work to build Arabic summarization systems include deep learning models [26; 27; 28; 29; 30], the Particle Swarm Optimization (PSO) method [20], clustering techniques [23; 24], a fuzzy logic approach to select important sentences [25], and graph-based approaches [7; 11]. Abstractive summarizing models for the Arabic text have been explored in [28; 29; 30; 31; 32]. [PERSON] and [PERSON] [31] proposed a graph embedding-based abstractive text summarization technique for the Arabic language. The model used a deep neural network to generate the summary. The study evaluated the model against the baseline word embedding technique word2 vec. The study employed the standard _ROUGE_ evaluation metric and subjective evaluation. The model achieved 21.4% better than word2 vec in the F1 measure. In [32], the authors developed an abstractive Arabic text summarization system using deep neural networks, including Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM). The study also employed AraBERT to preprocess text. Additionally, the study compared different word embedding mechanisms. The result showed that BiLSTM achieved the best performance, and regarding word embedding, models using skip-gram outperformed the models that used the continuous bag of words (CBOW). Graph-based approaches were applied in many Arabic document summarization tasks. [PERSON] et al. [7] proposed a system for Arabic text summarization by using PageRank with an additional feature: each node (sentence) within the graph has an initial value representing the number of nouns in the sentence. They created the EASC dataset, Essex Arabic Summaries Corpus, a collection of 153 articles divided into ten categories: art, music, tourism, technology, and others. The result showed improved performance compared to LexRank and TextRank, with an F score of 0.6799. [PERSON] et al. [16] created an Arabic News Texts (ANT) dataset for text summarization consisting of news articles collected from five sources: Al-Arabiya, BBC, CNN, France24, and SkyNews. The study applied some of the techniques used for ATS, including LexRank, TextRank, Luhn keyword-based method [17], and Latent Semantic Analysis (LSA) to the ANT dataset. The results show that the LexRank technique provides the best results, with a _BLEU_ score of 0.690 and a _ROUGE_ score reaching 0.972. [PERSON] et al. [33] continue the work of [16] using ANT and EASC [22] datasets. The study proposed two new techniques for text summarization that depend on analogical proportions and achieved a _ROUGE_-1 score of 0.75 and a _BLEU_ score of 0.47. A summary of selected related work is presented in Table 1 ordered by publishing date.