University of Qom Journal of Engineering Management and Soft Computing 3116-0158 6 2 2020 09 22 Proposed Method for Note Detection and Automatic Identification of the Melody Models (Gusheh) in Iranian Traditional Music with Micro Approach -روش پیشنهادی برای شناسایی خودکار گوشه‌ها در ردیف موسیقی سنتی ایرانی با رویکرد میکرو 113 138 1268 10.22091/jemsc.2018.1268 EN Amir Vafaeian Knowledge and Information Studies, Kharazmi University, Tehran, Iran Keivan Borna Assistant Prof., Computer Sciences, Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran Hamed Sajedi Assistant Prof., Electronic Engineering, Faculty of Engineering, Shahed University, Tehran, Iran Dariush Alimohammadi Assistant Prof., Knowledge and Information Studies, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran Pouya Sarai 5. Assistant Prof., Music Department, Faculty of Art, Islamic Azad University Central Tehran Branch, Tehran, Iran Journal Article 2019 03 15 One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-document textual summarization is to produce a pre-defined length summary from input textual documents while maximizing documents’ content coverage. This paper presents a new approach for textual document summarization based on paraphrasing and textual entailment relations and formulating the problem as an optimization problem. In this approach the sentences of input documents are clustered according to paraphrasing relation and then the entailment score and final score of a fraction of the header sentences of clusters which have the best score according to the user query is calculated. Finally, the optimization problem is solved via greedy and dynamic programming approaches and while selecting the best sentences, the final summary is generated. The results of implementing the proposed system on standard datasets and evaluation via ROUGE system show that the proposed system outperforms the state-of-the-art systems at least by 2.5% in average. https://jemsc.qom.ac.ir/article_1268_9ef849dfdbd07c11a6361504799c29e2.pdf