DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
Subject Areas : Natural Language Processing
Shima
Mehrabi
1
(University of Guilan)
Seyed Abolghassem
Mirroshandel
2
(University of Guilan)
Hamidreza
Ahmadifar
3
(University of Guilan)
Keywords: Artificial Neural Networks, , Deep Learning, , Text Stigmatization, , Multi-Documents, , Natural Language Processing, ,
Abstract :
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.
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