DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mehrabi Shima; MIRROSHANDEL SEYED ABOLGHASEM; Ahmadifar Hamidreza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2019 | Volume:7 | Issue:3 (27) Page(s): 204-214

Download Full-Text

View:

194

Download:

185

Cites:

Information Journal Paper

Title

DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System

Author(s)

Mehrabi Shima | MIRROSHANDEL SEYED ABOLGHASEM | Ahmadifar Hamidreza | Issue Writer Certificate

Keywords

Artificial Neural Networks

Deep Learning

Text Summarization

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.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Mehrabi, Shima, MIRROSHANDEL, SEYED ABOLGHASEM, & Ahmadifar, Hamidreza. (2019). DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 7(3 (27)), 204-214. SID. https://sid.ir/paper/332801/en

Vancouver: Copy

Mehrabi Shima, MIRROSHANDEL SEYED ABOLGHASEM, Ahmadifar Hamidreza. DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2019;7(3 (27)):204-214. Available from: https://sid.ir/paper/332801/en

IEEE: Copy

Shima Mehrabi, SEYED ABOLGHASEM MIRROSHANDEL, and Hamidreza Ahmadifar, “DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 7, no. 3 (27), pp. 204–214, 2019, [Online]. Available: https://sid.ir/paper/332801/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops