TY  - CHAP
PY  - 2014
SN  - 978-3-319-13646-2
T2  - Human-Inspired Computing and Its Applications
SE  - 14
VL  - 8856
T3  - Lecture Notes in Computer Science
A2  - Gelbukh, Alexander
A2  - Espinoza, FélixCastro
A2  - Galicia-Haro, SofíaN.
DO  - 10.1007/978-3-319-13647-9_14
TI  - A New Memetic Algorithm for Multi-document Summarization Based on CHC Algorithm and Greedy Search
UR  - http://dx.doi.org/10.1007/978-3-319-13647-9_14
PB  - Springer International Publishing
DA  - 2014/01/01
KW  - Multi-document summarization
KW  - Memetic algorithms
KW  - CHC algorithm
KW  - Greedy search
AU  - Mendoza, Martha
AU  - Cobos, Carlos
AU  - León, Elizabeth
AU  - Lozano, Manuel
AU  - Rodríguez, Francisco
AU  - Herrera-Viedma, Enrique
SP  - 125-138
LA  - English
AB  - Multi-document summarization has been used for extracting the most relevant sentences from a set of documents, allowing the user to more quickly address the content thereof. This paper addresses the generation of extractive summaries from multiple documents as a binary optimization problem and proposes a method, based on CHC evolutionary algorithm and greedy search, called MA-MultiSumm, in which objective function optimizes the lineal combination of coverage and redundancy factors. MA-MultiSumm was compared with other state-of-the-art methods using ROUGE measures. The results showed that MA-MultiSumm outperforms all methods on the DUC2005 dataset; and on DUC2006 the results are very close to the best method. Furthermore in a unified ranking MA-MultiSumm only was improved on by the DESAMC+DocSum method, which requires as many iterations of the evolutionary process as MA-MultiSumm. The experimental results show that the optimization-based approach for multiple document summarization is truly a promising research direction.
ER  -