Title: Multi-document-based text summarisation through deep learning algorithm

Authors: G. Padmapriya; K. Duraiswamy

Addresses: Department of Computer Science and Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, Tamil Nadu, India ' Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, K.S.R. Kalvi Nagar, Tiruchengode, Tamil Nadu, India

Abstract: The proposed approach is provided an effort in terms of deep leaning algorithm to retrieve an effective text summary for a set of documents. Basically, the proposed system consists of two phases such as training phase and the testing phases. The training phase is used for exploiting the three different algorithms to make the text summarisation process an effective one. Similar to every training phase, the proposed training phases is also possessed of known data and attributes. After that, the testing phase is implemented to test the efficiency of the proposed approach. For experimentation, we used four documents sets which are selected from the DUC (2002). The experimental evaluation showed expected results as, the average precision of 78%, the average recall of 1 and the average f-measure of 84%.

Keywords: particle swarm optimisation; text summarisation; deep learning algorithm.

DOI: 10.1504/IJBIDM.2020.107546

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.4, pp.459 - 479

Received: 01 Apr 2017
Accepted: 30 Oct 2017
Published online: 01 Jun 2020 *

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