ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction

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dc.contributor.authorBahloul, Belahcene
dc.contributor.authorAliane, Hassina
dc.contributor.authorBenmohammed, Mohamed
dc.date.accessioned2023-09-19T14:31:31Z
dc.date.available2023-09-19T14:31:31Z
dc.date.issued2020-04-19
dc.description.abstractAutomatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster-based, and graph-based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall-oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n-gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.
dc.identifier.citationExpert Systems, Vol. 37, N° 2, Avril 2020
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/978
dc.language.isoen
dc.publisherWiley
dc.subjectAutomatic system for Arabic single-document summarization
dc.subjectNatural language processing
dc.subjectData-driven
dc.subjectGraph theory
dc.subjectInformation extraction
dc.subjectText mining
dc.subjectTopic identification
dc.titleArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction
dc.typeArticle
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