Mendeley TY _ JOUR ID - 13980804193091 TI - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System JO - Journal of Information Systems and Telecommunication (JIST) JA - ES LA - en SN - 2322-1437 AU - Mehrabi Shima AU - Mirroshandel Seyed Abolghassem AU - Ahmadifar Hamidreza AD - University of Guilan AD - دانشگاه گیلان AD - University of Guilan Y1 - 2020 PY - 2020 VL - 27 IS - 7 SP - 204 EP - 214 KW - Artificial Neural Networks KW - KW - Deep Learning KW - KW - Text Stigmatization KW - KW - Multi-Documents KW - KW - Natural Language Processing KW - DO - N2 - With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. UR - rimag.ir/en/Article/15367 L1 - rimag.ir/en/Article/Download/15367 ER -