Arabic Text Summarization

The experience of writing in the satire genre, with its modern meaning as a tool for social-political critique, began concurrently with the Constitutional Movement. During those years, the press served as the primary medium and a suitable platform for publishing satire. The satirical pieces found in the newspapers Keshkul, Azerbaijan, Sur-e Esrafil, and Nasim-e Shomal, presented in both prose and verse, are evidence of this. The magazine Alame Nesvan (published from 1920 to 1932) released a series of humorous stories over its many years of publication. The signature of Delshad Khanoom Changizi at the end of these stories has been a subject of interest and challenge for researchers in the fields of literary history and particularly women's literature. Proving the true identity of the author as a woman is one of the main challenges of this topic. Based on existing evidence in documents and correspondence between the author and the magazine's editor, we do not dismiss the possibility of the author being a woman. However, the primary focus of this research is to describe and analyze the content, structure, and satirical techniques used by Delshad Khanoom Changizi. Our findings indicate that the narrative structure, common satirical techniques, and the content of her writings, such as references to women's education, criticism of superstitions, opposition to forced marriages, health certification of couples, support for lifting the veil, and the necessity of women's participation in national progress, could be indicative of a feminine writing tradition. Therefore, her writings can be introduced as the first female narrative satire in Persian press.

Computing the similarity between documents is an important operation in the text processing. In this paper, a new similarity measure is proposed. To calculate the similarity between two documents with respect to a feature, the proposed measure takes the following three cases in to account I) The same feature appears in both documents, II) The same feature appears in only one document, and III) The same feature appears in none of the documents. For the first case, the similarity will increases as the difference between the two involved feature values decreases. For the second case, a fixed value is involved to the similarity. For the last case, the feature has no appearance to the similarity. The proposed measure is extended to the similarity between the sets of documents. The effectiveness of our measure is computed on the number of data sets for text clustering and classification. The performance obtained by the proposed measure is better than achieved by other measures.

Article type: Research Article Grammatical metaphor is marked as one of the outstanding issues in the systematic grammar function. Creating a new semantic system with the usage of new functions is claimed as one of the most attractive facilities that the language may provide. In addition to creating attractive expressions, it expands the semantic values of the text. With that in mind, these types of figures of speech and their analyses lead to a better identification of the text in textual studies. Considering the fact that such grammatical metaphors are exquisitely used in the Persian book for third grade of elementary School, this article explores this figure of speech by describing and analyzing it in the stories of Persian books and the Iraqi book for third grade of elementary school. The research expresses the concept of grammatical metaphor on the sample of children's story. One of the major consequences of using grammatical metaphor of nominalization type is the coherence and magnification of the text. More precisely, nominalization hides the identity of the text doer and provokes the readers` thinking.

This study aims to know the effect of the flow of foreign investments on Egypt, especially after the economic experience of Muhammad Ali was hit and the foreigners obtained many privileges that had a direct impact on pumping their money and investing it in Egypt, which resulted in providing them with the opportunity to establish many companies contributing in the field of services and public facilities Such as water, gas and electricity in the city of Alexandria, the most important of which is the Alexandria Water Company, which was intended to provide pure water to the city's neighborhoods. The study also aims to shed light on the establishment of the English Alexandria Water Company and the mechanisms used by the company to provide water to the city's residents and how the Egyptian government had the right to control the activity The company's capital and the impact of the company's activity on the Alexandrian community . raising the health level. For city dwellers. The study also found that the July 1952 revolution had a direct impact on foreign investments when the Egyptian government egyptianized the company in 1958 and issued a law to nationalize it in July 1961, and convert it into an Egyptian joint stock company. The study relied mainly on documents such as the notebooks and documents of the Sunni authorities, Turkish and Arab, the minutes of the municipal and administrative commission sessions, the municipal commission of the city of Alexandria, the documents of the Alexandria governorate, and the reports and statistics of the jointstock companies whose main exploitation exists in Egyp

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two ti...

This paper introduces an automatic method to extend existing WordNets via machine translation. Our proposal relies on the hierarchical skeleton of the English Princeton WordNet (PWN) as a backbone to extend their taxonomies. Our proposal is applied to the Arabic WordNet (AWN) to enrich it by adding new synsets, and also by providing vocalizations and usage examples for each inserted lemma. Around 12000 new potential synsets can be added to AWN with a precision of at least 93%. As such the coverage of AWN in terms of synsets can be increased from 11269 to around 24000 a very promising achievement on the path of enriching the Arabic WordNet.

The big growth of the Arabic internet content in the last years has raised up the need for an effective stemming techniques for Arabic language. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja); stem-based approach (ex. Larkey); and statistical approach (ex. N-Garm). However, no stemming of this language is perfect: The existing stemmers have a low efficiency. In this paper, we introduce a new stemming technique for Arabic words that also solve the problem of the plural form of irregular nouns in Arabic language, which called broken plural. The proposed stem extractor provides very accurate results in comparisons with other algorithms.Consequently the search effectiveness improved.

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

In this paper is proposed the generic summarization method that extracts the most relevance sentences from the source document to form a summary. This method based on clustering of sentences. The specificity of this approach is that the generated summary can contain the main contents of different topics as many as possible and reduce its redundancy at the same time. The clustering method satisfies as much homogeneity within each cluster as well as much separability between the clusters as possible.

A wide variety of similarity measures have been used for clustering such as Jaccard coefficient similarity, Dice coefficient similarity and so on. From the fundamental assumption that similarity of context implies similarity of meaning, relevant Web documents are downloaded via a Web search engine and the contextual information of words of interest are compared. Here we compute the semantic similarity between words or terms are presented and compared with the state of the art. And from this we have to compare and analyze the effectiveness of these measures in clustering for text document datasets.

عمل علمي مقدم للمشاركة في ندوة وطنية حول الموضوع ، من تنظيم جمعية حورية للمراة الجزائرية بمركز العربي بلمهيدي -العاصمة، يقدم نتائج دراسة استطلاعية حول اهم الاسباب الممكن ان تؤدي الى بروز وانتشار هذه الظاهرة السلبية، الدخلية على ثقافة المجتمع الجزائري

In this study, we proposed an Arabic text summarization approach based on extractive graph-based approaches. In order to measure the efficiency of the proposed approach, several basic units such as stem, word, and n-gram are applied in the summarization process. The Arabic document is represented as a graph and we used the shortest path algorithm to extract the summary. Similarity between any two sentences is determined by ranking the sentences according to some statistical features. The final score is determined for each sentence using PageRank scoring, and finally, the sentences with high scores are included in the summary considering the compression ratio. The proposed approach is evaluated using EASC corpus, and intrinsic evaluation method. The results showed that the proposed approach achieved good results. Also, the results showed that the use of n-grams as a basic unit in summarization process achieved better results than the stem and word.

In this paper, a hybrid system which depending on syntactic-fuzzy system and Genetic Algorithm has been designed to generate an extractive summary. Text has been segmented into group of sentences that composed from it and syntactic analysis of input text has performed. Linguistic variables have been identified and optimal membership functions have been chosen depending on genetic algorithm. Initial solutions (chromosomes) have been encrypted, initial population has been generated and genetic operations have been applied. Evaluation has been done using fitness function to reach to the optimal solution which is the optimal membership functions of linguistic variables of syntactic-fuzzy expert system. Fuzzy rules have been designed for syntactic structures of Arabic words and sentences. Fuzzy inference engine applying hybrid inference method combining Depth-First and Breadth-First strategy has been designed.
Designed system has been tested on many Arabic texts according to different summarization ratios, and results have been evaluated using precision, recall and
F-measure criterions. After comparing the results of the hybrid system with the results of the syntactic-fuzzy system designed in previous research, improvement in the accuracy of the summarization has been showed, which caused by integration of the Genetic Algorithm with the syntactic-fuzzy system. Remembering that the hybrid system has performed summarization just from syntactic point of view, very positive and promising results have been gotten. Resulted summaries also, have been identical to summaries which have gotten from human experience.

Arabic texts summarization has become an important and urgent field of research, as a result of inflation in the size of the Arabic texts in digital format.
In this paper, a novel technique is presented for Arabic texts summarization. Syntactic-fuzzy expert system has been designed to generate an abstractive summary of short, information rich and meaning-focus sentences.
Syntactic analysis of input text has performed, linguistic variables of fuzzy system have identified and optimal membership functions (MFs) have chosen. The selection of MFs has done corresponding to definition of Arabic language grammar, syntactic components of sentences and Arabic syntactic frames which have certified by Arabic Language Academy. Fuzzy inference engine applying hybrid inference strategy combining both Depth-First Search (DFS) and Breadth-First Search (BFS) strategy has designed.
Designed system has tested on many Arabic texts. Remembering that the system has performed summarization just from syntactic point of view, very positive and promising results have been gotten. These results open a broad horizon for understanding and auto processing of Arabic text. Resulted summaries also, have been identical to summaries which have gotten from human experience.

As a result of fast-growing in the size of the Arabic texts in digital format on the Web. The need has become urgent for the provision of applications and techniques that enable the user to estimate the degree of document appropriation for the information they need, taking into consideration that the required information is available in generated summary. In this paper, syntactic Arabic text summarization system has been designed and implemented depending on syntactic analyzing. In this phase syntactic structures tree of input text has been built using rewrite rules (BNF). Text has been segmented into groups of simple and combined sentences. Syntactic components have been weighted using knowledge rules which have based on occurrence ratio of syntactic components, and human experience in this field. Fuzzy system containing fuzzy learning rules and applying hybrid inference strategy has been designed. The purpose of fuzzy system is to find out the acceptability of sentence from the syntactic point of view. Clustering algorithm (K-Means) has been applied to get Centroid Vector (CV) of text sentences and then calculate the Euclidean Distance between text sentences and CV. Designed system has been tested on many Arabic texts, taking into account that it summarizes texts based on syntactic information only. Results of this new system have proved that application of hybrid system based on number techniques lead to very positive and promising results, which open a broad horizon for understanding and auto processing of Arabic text. The system summaries also, have been identical to summaries which have been gotten from human experience.

Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores.

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

applications. More precisely, the purpose is to better take into account the semantic dependencies between words expressed by the co-occurrence frequencies of these words. There have been many proposals to compute similarities between words based on their distributions in contexts. In this paper, we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Rootbased (Stemming)

In this paper, a new multidocument multi-lingual text summarization technique, based on singular value decomposition and hierarchical clustering, is proposed. The proposed approach relies on only two resources for any language: a word segmentation system and a dictionary of words along with their document frequencies. The summarizer initially takes a collection of related documents, and transforms them into a matrix; it then applies singular value decomposition to the resulted matrix. After using a binary hierarchical clustering algorithm, the most important sentences of the most important clusters form the summary. The appropriate place of each chosen sentence is determined by a novel technique. The system has been successfully tested on summarizing several Persian document collections.

In this paper, a new multidocument multi-lingual text summarization technique, based on singular value decomposition and hierarchical clustering, is proposed. The proposed approach relies on only two resources for any language: a word segmentation system and a dictionary of words along with their document frequencies. The summarizer initially takes a collection of related documents, and transforms them into a matrix; it then applies singular value decomposition to the resulted matrix. After using a binary hierarchical clustering algorithm, the most important sentences of the most important clusters form the summary. The appropriate place of each chosen sentence is determined by a novel technique. The system has been successfully tested on summarizing several Persian document collections.

Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores.