A Comprehensive Review of Arabic Text Summarization

Abstract

The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language's morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.

A Hybrid Arabic text summarization Approach based on Seq-to-seq and Transformer

“…The experimental result of the proposed model for abstractive text summarization using evaluation metrics Rouge1, Rouge2, Rouge-l, Bleu, and Arabic Rouge for the EASC dataset was 0.587, 0.48, 0.56, 0.42, and 0.652, while for the HASD dataset, they were 0.6374, 0.4908, 0.6047, 0.44, and 0.713 As shown in tables (16and17). As shown in table (19), we compare our proposed system to other abstract Arabic summarizations that use deep learning. When we compare our proposed system to those from other research studies, we nd that ours has the highest levels of Rouge1, Rouge2, Rouge-l, and Bleu, which means that its summary is the best.…”

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

“…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, Elsaid 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.…”

“…ATS aims to produce a smooth and short summary that includes important information from the original text [2]. Several studies addressed ATS for English documents [3,4]; however, fewer studies are available for the Arabic language due to its rich morphological structure, the range of dialects, and the scarcity of data and tools [5]. Arabic is the fifth most spoken language globally, with more than 400 million speakers worldwide [6].…”

“…ATS can be classified according to the number of simultaneously processed singleand multi-document documents [5]. Single-document summarization is an automatic procedure focusing on extracting useful information from a single text, such as an article, research, or webpage [8].…”

“…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].…”