A Hybrid Arabic Text Summarization Approach based on Transformers

Reda, Ameen; Salah, Nada; Adel, John; Ehab, Mohamed; Ahmed, Ifty; Mostafa, Mona; Khoriba, Ghada; Mohamed, Ensaf Hussein

doi:10.1109/miucc55081.2022.9781694

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…The experimental results on EASC and their datasets, using the Rouge metric for evaluation of the dataset, are as follows: for EASC, Rouge-1 is 63.19; for the second dataset, Rouge-1 is 68.40. (Reda et al 2022) proposed a hybrid Arabic text summarization system, A3SUT, based on a transformer by using AraBert for extractive summarization and a T5 Arabic pre-trained transformer for abstractive summarization, and they tested it on two datasets: the EASC and Nada corpora. The proposed system is evaluated by a Rouge1 precision of 0.5348, a recall of 0.5515, and an F1 score of 0.4932 for extractive summarization.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Elsaid

Mohammed

Fattouh

et al. 2023

Preprint

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Text summarization is essential in natural language processing as the data volume increases quickly. Therefore, the user needs to summarize that data into a meaningful text in a short time. There are two common methods of text summarization: extractive and abstractive. There are many efforts to summarize Latin texts. However, summarizing Arabic texts is challenging for many reasons, including the language’s complexity, structure, and morphology. Also, there is a need for benchmark data sources and a gold standard Arabic evaluation metrics summary. Thus, the contribution of this paper is multi-fold: First, the paper proposes a hybrid approach consisting of a Modified Sequence-To-Sequence (MSTS) model and a transformer-based model. The seq-to-seq-based model is modified by adding multi-layer encoders and a one-layer decoder to its structure. The output of the MSTS model is the extractive summarization. To generate the abstractive summarization, the extractive summarization is manipulated by a transformer-based model. Second, it introduces a new Arabic benchmark dataset, called the HASD, which includes 43k articles with their extractive and abstractive summaries. Third, this work modifies the well-known extractive EASC benchmarks by adding to each text its abstractive summarization. Finally, this paper proposes a new measure called the Arabic-rouge measure for the abstractive summary depending on structure and similarity between words. The proposed method is tested using the proposed HASD and Modified EASC benchmarks and evaluated using Rouge, Bleu, and Arabic Rouge. The experimental results show satisfactory results compared to state-of-the-art methods.

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Section: Literature Reviewmentioning

confidence: 99%

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

Elsaid

Mohammed

Fattouh

et al. 2023

Preprint

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“…Hybrid summarization combines extractive with abstractive. The method has the drawback of producing lowerquality abstractive summaries compared to the pure abstractive approach [2], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Indonesian News Text Summarization Using MBART Algorithm

Astuti,

Muljono,

Sutriawan

2024

SJI

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Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.

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Extractive Arabic Text Summarization Using PageRank and Word Embedding

Alselwi,

Taşcı

2024

Arab J Sci Eng

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Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and PageRank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%.

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A Hybrid Arabic Text Summarization Approach based on Transformers

Cited by 10 publications

References 7 publications

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

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

Indonesian News Text Summarization Using MBART Algorithm

Extractive Arabic Text Summarization Using PageRank and Word Embedding

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