Multilingual Text Summarization Based On LDA And Modified Pagerank

Ali, Zuhair Hussein; Malallah, Suhad

doi:10.34279/0923-009-003-013

Cited by 5 publications

(4 citation statements)

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“…Reference [40] They propose a multilingual text summarization approach that is based on LDA, linear discriminant analysis, and modified PageRank. They used the k-means clustering technique to choose the essential sentences based on similarity metrics from a document set written in seven languages: English, Arabic, Greek, French, Hindi, and Hebrew.…”

Section: Deep Learning-based Atsmentioning

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: Deep Learning-based Atsmentioning

confidence: 99%

“…RNN [37], LSTM[38], Encode-Decode[39],[40], Attention[39], Transformer, Bert[22],[41]. Moreover, at the start of the model in the year 2021, the researchers published a new language model named Pegasus, and they evaluated this model in this field of text summarization.…”

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confidence: 99%

A Comprehensive Review of Arabic Text Summarization

et al. 2022

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“…In [13], Ali proposed the use of LDA and modified PageRank to summarize Arabic and English documents. LDA algorithm is used to classify sentences as important or unimportant based on seven extracted features.…”

Section: Statistical-based Approachesmentioning

confidence: 99%

“… All studies are evaluated their proposed approaches using the same metrics (precision, recall, and f-score).  One study [13] used the TAC dataset.  One study [10] used an artificial dataset consisting of 33 short documents collected from the Wikipedia.…”

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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Extractive text summarization of arabic multi-document using fuzzy C-means and Latent Dirichlet Allocation

Al-Taani

Al-Sayadi

2022

Int J Syst Assur Eng Manag

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Multilingual Text Summarization Based On LDA And Modified Pagerank

Cited by 5 publications

References 0 publications

A Comprehensive Review of Arabic Text Summarization

A Comprehensive Review of Arabic Text Summarization

Recent Advances in Arabic Automatic Text Summarization

Extractive text summarization of arabic multi-document using fuzzy C-means and Latent Dirichlet Allocation

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