# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 2 Background  
ATS refers to one of the natural language processing (NLP) applications that aim to reduce the amount of original text and retrieve important and essential information from the original text [7]. ATS produces a new version of the text, preserving the original text's context and original meaning [13]. ATS procedure can be classified according to the following criteria:  
* The number of documents simultaneously processed--single- and multi-document [9].
* The type of retrieved sentences, extractive abstractive, and hybrid summarization [5]. Extractive summarization selectively includes in the summary some sentences from the original text without modifying the sentence structure, while abstractive summarization maintains the text meaning without adhering to its structure.
* General-based and query-based summary; the general-based summary retrieves the sentence regardless of any consideration for question or relations with the title, whereas the query-based summary is returned due to the summary and questions between the title and sentence [2].  
Summarizing reviews can be viewed as a multi-document summarization process. Extractive summarization comprises four main methods, graph-based, statistical-based, fuzzy logic, and semantic-based [5]. In this study, we consider graph-based extractive summarizations as extractive methods, in general, yield better outcomes compared to abstractive methods, which still face many challenges, such as the difficulty of semantic representation, inference, and natural language generation [14]. Graph-based approaches are entirely unsupervised, so no training data are required. A document is reconstructed as a graph, where nodes are sentences and edges are similarities between nodes. The fundamental graph-based methods are described next.  
* PageRank: PageRank is the backbone of the infamous search engine Google, created by [PERSON] and [PERSON] in 1998. Google revolutionized web search by introducing PageRank, which finds web pages not only by considering keywords and indexing but also according to the page importance compared to other web pages. In this algorithm, the web pages are represented by nodes, and edges represent the links between pages. A page with a high rank is a page with a high in-degree value, i.e., many other pages are pointing to it. Additionally, this is done recursively, so if the edge comes from a significant page, it will have extra importance.
* TextRank: PageRank is the heart of TextRank. TextRank is a graph-based ranking model developed by [PERSON] and [PERSON] [15] for extracting important keywords and sentences from the text. TextRank is considered an extractive text summarization method. It generates a fully connected, undirected graph of sentences or words of a single document. In this graph, each sentence within the document is represented by a node. In contrast, the similarity between sentences measured as a function of joint concepts is defined by an edge. A weight value marks the importance of each edge. The sentences are ordered based on their scores, thus, creating a list of ranked sentences. For text summarization, usually, the top-ranked sentences are the most representative.
* LexRank: a graph-based extractive text summarization technique [14], similar to TextRank, where a node represents each sentence; however, the edge weights are defined by computing the cosine similarities. LexRank uses graph centrality measures such as degree and eigenvector centrality to find important sentences. During the summarization process, the relevance between the sentences is considered by calculating their importance for neighboring sentences. The positive contribution enhances the importance value of the neighbor sentence, and the negative contribution minimizes the importance value of neighbor sentences. Then, the sentences are ordered based on rank [16].