Article
Journal :
International Journal of Natural Computing Research (IJNCR)
ISSN : 1947-928X
Publisher :
Information
Year : 2021
Volume : 10 Number : 2
Pages : 42-60
Details
A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization
Khadidja Chettah Amer Draa
Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.
Ref. laboratory citation :
misc-lab-373
DOI :
10.4018/IJNCR.2021040103
Link :
Texte intégral
ACM :
K. Chettah and A. Draa. 2021. A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization. International Journal of Natural Computing Research (IJNCR), 10, 2, IGI Global, 42-60. DOI: https://doi.org/10.4018/IJNCR.2021040103.
APA :
Chettah, K. & Draa, A. (2021). A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization. International Journal of Natural Computing Research (IJNCR), 10(2), IGI Global, 42-60. DOI: https://doi.org/10.4018/IJNCR.2021040103
IEEE :
K. Chettah and A. Draa, "A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization". International Journal of Natural Computing Research (IJNCR), vol. 10, no. 2, IGI Global, pp. 42-60, 2021. DOI: https://doi.org/10.4018/IJNCR.2021040103.
BibTeX :
@article{misc-lab-373,
author = {Chettah, Khadidja and Draa, Amer},
title = {A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization},
journal = {International Journal of Natural Computing Research (IJNCR)},
volume = {10},
number = {2},
issn = {1947-928X},
pages = {42--60},
publisher = {IGI Global},
year = {2021},
doi = {10.4018/IJNCR.2021040103},
url = {https://doi.org/10.4018/IJNCR.2021040103}
}
RIS :
TI  - A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization
AU - K. Chettah
AU - A. Draa
PY - 2021
SN - 1947-928X
JO - International Journal of Natural Computing Research (IJNCR)
VL - 10
IS - 2
SP - 42
EP - 60
PB - IGI Global
AB - Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.
DO - 10.4018/IJNCR.2021040103
UR - https://doi.org/10.4018/IJNCR.2021040103
ID - misc-lab-373
ER -