Single Arabic Document Summarization Using Natural Language Processing Technique

Bialy, Asmaa Awad A.; Gaheen, Marwa A.; ElEraky, Rania M.; Elgamal, A. F.; Ewees, Ahmed A.

doi:10.1007/978-3-030-34614-0_2

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…The model employs the numeric approach but for the Giga word. TF-IDF BERT, fine-tuning [34]Analogical Proportions [35] Word2Vec and Clustering [28]Natural Language Processing [18] F-RBM [18] Clustering algorithm are based on the Ara BERT model [36].To reduce redundancy, multi-document Arabic text summarization based on clustering and Word2Vec is used. Automatic Summarization of Arabic Documents use Unsupervised Deep Learning New approaches to the age of automated headline features in Arabic documents [29].Many new algorithms are also there to solve the problem of text summarization, like AI Language specialists distributed another pre-prepared language portrayal technique.…”

Section: ) Semantic-based Methodsmentioning

confidence: 99%

“…Summarization can also be divided based on the length and type of the input document. There are two commonly used terms: single document summarization, which focuses on input that consists of only one document, which is usually not very long [18] and multi-document summarization, which takes an input collection of documents and attempts to construct a summary from the set as a whole rather than summarizing each input document individually, which may result in a lot of redundancy [19]. If the reader is interested in a specific topic, some systems may accept a search query and specifically produce a summary focused on the parts of the original text that are most relevant to the search query.…”

Section: F Evaluationmentioning

confidence: 99%

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A Comprehensive Review of Arabic Text Summarization

et al. 2022

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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.

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Section: ) Semantic-based Methodsmentioning

confidence: 99%

Section: F Evaluationmentioning

confidence: 99%

A Comprehensive Review of Arabic Text Summarization

et al. 2022

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“…A statistical-based extractive approach for Arabic single-document summarization is suggested by Bialy et al [10]. The proposed approach comprised of three phases, a pre-processing, scoring of sentences, and summary generation.…”

Section: Statistical-based Approachesmentioning

confidence: 99%

“… One study [13] used the TAC dataset.  One study [10] used an artificial dataset consisting of 33 short documents collected from the Wikipedia. It is difficult to assess the significance of this study since it is not compared with other approaches and does not use a standard dataset as other studies.…”

Section: Statistical-based Approachesmentioning

confidence: 99%

Recent Advances in Arabic Automatic Text Summarization

Al-Taani¹

2021

IJASCA

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Recently, the volume of the Arabic texts and documents on the internet had increased rabidly and generated a rich and valuable content on the www. Several parties had contributed to this content, this includes researchers, companies, governmental agencies, educational institutions, etc. With this big content it became difficult to search and extract useful information using only mankind skills and search engines. This motivated researchers to propose automated methodologies to extract summaries or useful information from those documents. A lot of research has been proposed for the automatic extraction of summaries for the English language and other languages. Unfortunately, the research for the Arabic automatic text summarization is still humble and needs more attention. This study presents a critical review and analysis of recent studies in Arabic automatic text summarization. The review includes all recent studies used the different text summarization approaches which include statistical-based, graph-based, evolutionary-based, and machine learning-based approaches. The selection criteria of the literature are based on the venue of publication and year of publication; back to five years. All review papers in Arabic ATS are excluded from the review since the study considers the recent methodologies in Arabic ATS. As a conclusion of this research, we recommend researchers in Arabic text summarization to investigate the use of machine learning on abstractive approach for text summarization due to the lack of research in this area. Keywords: Automatic Text Summarization, The Arabic Language, Machine Learning, Natural Language Processing, Text Processing, Computational Linguistics.

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“…, Sn), where Si represents the i th sentence in D, and n represents the total number of sentences in each document. The goal of the final summary is to select the set of sentences from MD covering various related topics from related documents, [20].…”

Section: The Challenge Of the Redundancymentioning

confidence: 99%

Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy

et al. 2020

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Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.

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Single Arabic Document Summarization Using Natural Language Processing Technique

Cited by 8 publications

References 33 publications

A Comprehensive Review of Arabic Text Summarization

A Comprehensive Review of Arabic Text Summarization

Recent Advances in Arabic Automatic Text Summarization

Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy

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