AZOM: a Persian structured text summarizer
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
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The importance of text summarization grows rapidly as the amount of information increases exponentially. This paper presents a new hybrid summarization technique that combines statistical properties of documents with Farsi linguistic features. The originality of the technique lies on the use of term co-occurrence property of the text. It could detect the number of subjects. The proposed technique summarizes the document in proportion to the subject treated in a document. It considers the conceptual property of the text algorithm and based on word synonymy prevents similar sentences to be included in the summary. It also preserves the cohesion of the summarized text. Our results show better performance in comparison with FarsiSum, well known Farsi Summarizer, which is based only on the heuristic property of the text and do not consider the Farsi challenges.