An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization

Chouigui, Amina; Khiroun, Oussama Ben; Elayeb, Bilel

doi:10.1007/s13369-020-05258-z

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…(7) Text Summarisation. This task includes five publicly available datasets, including both Arabic and multilingual data: MassiveSum (Varab and Schluter, 2021), XLSum Hasan et al (2021), Cross-Sum (Bhattacharjee et al, 2021), ANT (Chouigui et al, 2021), and MarSum (Gaanoun et al, 2022).…”

Section: Unlabled Datamentioning

confidence: 99%

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation

Elmadany,

Nagoudi,

Abdul-Mageed

2023

Proceedings of ArabicNLP 2023

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Understanding Arabic text and generating human-like responses is a challenging endeavor. While many researchers have proposed models and solutions for individual problems, there is an acute shortage of a comprehensive Arabic natural language generation toolkit that is capable of handling a wide range of tasks. In this work, we present a novel Arabic text-to-text Transformer model, namely AraT5 v2 . Our new model is methodically trained on extensive and diverse data, utilizing an extended sequence length of 2, 048 tokens. We explore various pretraining strategies including unsupervised, supervised, and joint pertaining, under both single and multitask settings. Our models outperform competitive baselines with large margins. We take our work one step further by developing and publicly releasing OCTOPUS, a Pythonbased package and command-line toolkit tailored for eight Arabic generation tasks all exploiting a single model. We release the models and the toolkit on our public repository. 1

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Section: Unlabled Datamentioning

confidence: 99%

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation

Elmadany,

Nagoudi,

Abdul-Mageed

2023

Proceedings of ArabicNLP 2023

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“…Text summarization research has been around since 1958 [17], where the problem was approached considering the frequency of terms in a document. As tools and techniques developed, recent studies applied various methods, from word and phrase counts to deep learning architectures and graph-based methods.…”

Section: Related Workmentioning

confidence: 99%

“…Chouigui et al [16] created an Arabic News Texts (ANT) dataset for text summarization consisting of news articles collected from five sources: Al-Arabiya, BBC, CNN, France24, and SkyNews. The study applied some of the techniques used for ATS, including LexRank, TextRank, Luhn keyword-based method [17], and Latent Semantic Analysis (LSA) to the ANT dataset. The results show that the LexRank technique provides the best results, with a BLEU score of 0.690 and a ROUGE score reaching 0.972.…”

Section: Related Workmentioning

confidence: 99%

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

2022

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The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.

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“…Concerted efforts have been made to overcome those challenges by building various Arabic datasets for the task such that EASC (El-Haj et al, 2010), Kalimat(El-Haj and Koulali, 2013), TAC2011 (El-Ghannam and El-Shishtawy, 2014), ANT (Chouigui et al, 2021), and XL-Sum (Hasan et al, 2021), but those datasets have limitations in terms of diversity or size. Therefore, the demand for a diverse and large-scale dataset is crucial to advance the ATS field.…”

Section: Introductionmentioning

confidence: 99%

“…Arabic News Texts Corpus (ANT) and XL-Sum. ANT (Chouigui et al, 2021), and XL-Sum (Hasan et al, 2021) are the most recent works. ANT collected 31,798 documents paired with summaries using RSS feeds from 5 Arab news sources: AlArabiya, BBC, CNN, France24, and SkyNews, while XL-Sum collected 40,327 only from BBC.…”

Section: Introductionmentioning

confidence: 99%

LANS: Large-scale Arabic News Summarization Corpus

Alhamadani,

Zhang,

et al. 2023

Proceedings of ArabicNLP 2023

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Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing stages. Existing ATS datasets are either small or lack diversity. We build, LANS, a largescale and diverse dataset for Arabic Text Summarization task. LANS offers 8.4 million articles and their summaries extracted from newspapers websites' metadata between 1999 and 2019. The high-quality and diverse summaries are written by journalists from 22 major Arab newspapers, and include an eclectic mix of at least more than 7 topics from each source. We conduct an intrinsic evaluation on LANS by both automatic and human evaluations. Human evaluation of 1,000 random samples reports 95.4% accuracy for our collected summaries, and automatic evaluation quantifies the diversity and abstractness of the summaries.

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An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization

Cited by 19 publications

References 35 publications

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

LANS: Large-scale Arabic News Summarization Corpus

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