Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering

Froud, Hanane; Lachkar, Abdelmonaime; Ouatik, Said Alaoui

doi:10.48550/arxiv.1302.1612

Cited by 3 publications

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“…The Arabic Morpho syntacƟc Markers and FuncƟonal Words ‫ﻓﻲ،ﻭ،ﺛﻡ،ﺃﻭ،ﺃﻡ،ﺑﻝ،ﻟﻛﻥ،ﻟﻛﻥ،ﺣﺗﻰ‬ In, and, then, or, but, when ‫ﻭﻟﻳﺱ‬ ، ‫ﻗﺑﻝ،ﻭﻟﻬﺫﺍ‬ ‫ﺑﻌﺩ،ﺑﺎﻟﺭﻏﻡ،ﺣﻳﺙ،‬ ‫ﺍﻳﺿﺎ،‬ Also, aŌer, although, as before, but this, not Fig. 2 Samples of Arabic morpho-syntactic markers and functional words [23] A morphological decomposition based on punctuation and decomposition based on the recognition of markers morphosyntactic or functional words such as: or, and, but, when. However, these particles may play a role other than to separate phrases as shown in Fig.…”

Section: ‫ﺍﻟﻌﺭﺑﻳﺔ‬ ‫ﺍﻟﻠﻐﺔ‬ ‫ﻓﻲ‬ ‫ﻭﺍﻟﻭﺻﻝ‬ ‫ﺍﻟﺭﺑﻁ‬ ‫ﺍﺩﻭﺍﺕ‬mentioning

confidence: 99%

“…They used LSA to measure the similarity between the extracted sentences to reduce the redundancy in the generated summary. The impact of Arabic text summarization on Arabic document clustering has been investigated by Froud et al [23]. They used LSA to generate Arabic summaries, then they used those summaries to represent the document in Vector Space Model to enhance the Arabic document clustering.…”

Section: Arabic Lsa Modelsmentioning

confidence: 99%

“…Different approaches for Arabic stemming can be identified, such as manually constructed dictionaries, algorithmic light stemmer, and morphological analyzers. Al-Fedaghi and Al-Anzi [23] algorithms try to find the root of the word by matching the word with all possible patterns and affixes attached to it. In this paper, ISRI Arabic Stemmer in python NLTK is used to stem Arabic words.…”

Section: (1) Arabic Word Morphologymentioning

confidence: 99%

“…The aim of the proposed algorithm is to select candidate sentences for the summary using the three matrices generated by SVD. The complexity depends on the execution time of the innermost loop in Algorithm 1 (line [17][18][19][20][21][22][23][24][25]. That loop basically selects sentences for the summary.…”

Section: Algorithm Description and Complexity Analysismentioning

confidence: 99%

“…Therefore, the complexity depends on the execution time of the basic operation which is the selection of the candidate sentences for the summary. Selecting M sentences for the summary takes at most O(n) + O(P k ) + O(M), where O(n) is the execution time for the first loop in the algorithm, lines (2-5), O(P k ) is the execution time of the inner loop, lines (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28) and O(M) is the execution time for the main loop, lines (8-31); hence, the overall time complexity of the algorithm is estimated as O(n).…”

Section: Algorithm Description and Complexity Analysismentioning

confidence: 99%

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An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods.

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Section: ‫ﺍﻟﻌﺭﺑﻳﺔ‬ ‫ﺍﻟﻠﻐﺔ‬ ‫ﻓﻲ‬ ‫ﻭﺍﻟﻭﺻﻝ‬ ‫ﺍﻟﺭﺑﻁ‬ ‫ﺍﺩﻭﺍﺕ‬mentioning

confidence: 99%

Section: Arabic Lsa Modelsmentioning

confidence: 99%

Section: (1) Arabic Word Morphologymentioning

confidence: 99%

Section: Algorithm Description and Complexity Analysismentioning

confidence: 99%

Section: Algorithm Description and Complexity Analysismentioning

confidence: 99%

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An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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Automatic summarization of YouTube video transcription text using term frequency-inverse document frequency

Albeer

AL-Shahad

Aleqabie

et al. 2022

IJEECS

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Automatic summarization <span>is a technique for quickly introducing key information by abbreviating large sections of material. Summarization may apply to text and video with a different method to display the abstract of the subject. Natural language processing is employed in automated text summarization in this research, which applies to YouTube videos by transcribing and applying the summary stages in this study. Based on the number of words and sentences in the text, the method term frequency-inverse document frequency (TF-IDF) was used to extract the important keywords for the summary. Some videos are long and boring or take more time to display the information that sometimes finds in a few minutes. Therefore, the essence of the proposed system is to find the way to summarize the long video and introduce the important information to the user as a text with few numbers of lines to benefit the students or the researchers that have no time to spend with long videos for extract the useful data. The results have been evaluated using Rouge method on the convolutional neural network (CNN)-dailymail-master data set.</span>

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Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Yaseen,

Al-Khulaidi

2023

jast

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Arabic information retrieval is challenging due to the language's complex morphology and syntax. Preprocessing and stemming improve the accuracy and efficiency of Arabic information retrieval. This paper provides a comprehensive analysis of the existing literature on Arabic preprocessing and stemming techniques. The paper identifies the limitations and challenges of these techniques. The paper emphasizes the importance of preprocessing and stemming and underscores the need for further research to improve Arabic information retrieval. This study evaluates ten stemmers on a public dataset. The results show that root-based stemmers: Lucene, and khoja got the highest reduction rate 90.9%, and 85% respectively. The results emphasize that root-based stemmers have good conflating ability for similar terms, while light-based stemmers under-stem similar terms.

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Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering

Cited by 3 publications

References 0 publications

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

Automatic summarization of YouTube video transcription text using term frequency-inverse document frequency

Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

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