Related Terms Extraction from Arabic News Corpus Using Word Embedding

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

doi:10.1007/978-3-030-11683-5_26

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…In this section, we first present the test collections used to evaluate the proposed algorithms. As there is no gold test collection to be used for the evaluation of Arabic text summarizers [27], we choose to test with our ANT corpus [19][20][21] that we present in the next sub-section. We also test our summarizers using the collection EASC [27].…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…To evaluate our proposed methods for automatic extractive text summarization, we have used the dataset ANT corpus v2.1 (Arabic News Texts Corpus) which is freely available online 4 (other versions of ANT corpus are also available [19][20][21]). The ANT corpus v2.1 includes data from five different news sources: AlArabiya 5 , BBC 6 , CNN 7 , France24 8 and SkyNews 9 .…”

Section: Ant Corpus Data Collectionmentioning

confidence: 99%

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Automatic Arabic Text Summarization Using Analogical Proportions

et al. 2020

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Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive single-document summarization approaches based on analogical proportions which are statements of the form "a is to b as c is to d". The goal is to study the capability of analogical proportions to represent the relationship between documents and their corresponding summaries. For this purpose, we suggest two algorithms to quantify the relevance/irrelevance of an extracted keyword from the input text, to build its summary. In the first algorithm, the analogical proportion representing this relationship is limited to check the existence/non-existence of the keyword in any document or summary in a binary way without considering keyword frequency in the text, whereas the analogical proportion of the second algorithm considers this frequency. We have assessed and compared these two algorithms with some languageindependent summarizers (LexRank, TextRank, Luhn and LSA (Latent Semantic Analysis)) using our large corpus ANT (Arabic News Texts) and a small test collection EASC (Essex Arabic Summaries Corpus) by computing ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (BiLingual Evaluation Understudy) metrics. The best-achieved results are ROUGE-1 = 0.96 and BLEU-1 = 0.65 corresponding to educational documents from EASC collection which outperform the best LexRank algorithm. The proposed algorithms are also compared with three other Arabic extractive summarizers, using EASC collection, and show better results in terms of ROUGE-1 = 0.75 and BLEU-1 = 0.47 for the first algorithm, and ROUGE-1 = 0.74 and BLEU-1 = 0.49 for the second one. Experimental results show the interest of analogical proportions for text summarization. In particular, analogical summarizers significantly outperform three among four language-independent summarizers in the case of BLEU-1 for ANT collection and they are not significantly outperformed by any other summarizer in the case of EASC collection.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

Section: Ant Corpus Data Collectionmentioning

confidence: 99%

Automatic Arabic Text Summarization Using Analogical Proportions

et al. 2020

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“…Number of words in the collected corpora is 1,099,054,713 word in different domains. These sources are AL SHAMILA LIBRARY (994,011,955 words) collected from Islamic books, ANT Corpus v1.0 and v1.1 [21,22] (1,474,000 words) collected from news articles of Tunisian web radio site Jawhara FM, ANT Corpus v2 [23] (9,670,000 words) from News articles and, AraCorpus (93,898,758 words) collected from News and essays articles.…”

Section: Data Collectionmentioning

confidence: 99%

Corpus-based technique for improving Arabic OCR system

Aliwy

Al-Sadawi

2021

IJEECS

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<p><span>An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. </span></p>

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