Abstract
Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days. In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction
Highlights
Present days, growing of information exponentially in Web initiates information overload problem
The Document Understanding Conference (DUC) datasets i.e., DUC2006 and DUC2007 are distributed through ACQUINT, and used for this experimental study
ROUGE-1 score of all summarizer are falling within the range 0.41 to 0.44 and with respect to ROUGE-2 it is within the range 0.07 to 0.16 for DUC 2006 and DUC2007 dataset respectively
Summary
Present days, growing of information exponentially in Web initiates information overload problem. Further to improve the performance motivations have to be made on population diversity in the progressive procedure and a sophisticated approach for information sharing among each participant in the distribution To overcome these issues an evolutionary approach called Shuffled frog leaping algorithm (SFLA) is proposed. Author has proposed a variant of SFL called levy flight based shuffle frog leaping algorithm The effectiveness of this technique is explored using 30 benchmark function and six continuous optimization functions. As a stochastic search based learning technique, Sharma et al.[12], has suggested a varient of shuffle frog leaping algorithm called as centroid mutated SFLA for both discrete and continuous optimization problem. Inspired from the successful implementation of SFLA in many application areas as an optimization approach, in this study a novel Modified Shuffled frog leaping algorithm based multi document summarizer is presented.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
