@inproceedings{mishra-etal-2021-wikipedia,
title = "{W}ikipedia Current Events Summarization using Particle Swarm Optimization",
author = "Mishra, Santosh Kumar and
Kaushik, Darsh and
Saha, Sriparna and
Bhattacharyya, Pushpak",
editor = "Bandyopadhyay, Sivaji and
Devi, Sobha Lalitha and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2021",
address = "National Institute of Technology Silchar, Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-main.54",
pages = "447--455",
abstract = "This paper proposes a method to summarize news events from multiple sources. We pose event summarization as a clustering-based optimization problem and solve it using particle swarm optimization. The proposed methodology uses the search capability of particle swarm optimization, detecting the number of clusters automatically. Experiments are conducted with the Wikipedia Current Events Portal dataset and evaluated using the well-known ROUGE-1, ROUGE-2, and ROUGE-L scores. The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods. It attained improvement of 33.42{\%}, 81.75{\%}, and 57.58{\%} in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.",
}
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<abstract>This paper proposes a method to summarize news events from multiple sources. We pose event summarization as a clustering-based optimization problem and solve it using particle swarm optimization. The proposed methodology uses the search capability of particle swarm optimization, detecting the number of clusters automatically. Experiments are conducted with the Wikipedia Current Events Portal dataset and evaluated using the well-known ROUGE-1, ROUGE-2, and ROUGE-L scores. The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods. It attained improvement of 33.42%, 81.75%, and 57.58% in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.</abstract>
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%0 Conference Proceedings
%T Wikipedia Current Events Summarization using Particle Swarm Optimization
%A Mishra, Santosh Kumar
%A Kaushik, Darsh
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F mishra-etal-2021-wikipedia
%X This paper proposes a method to summarize news events from multiple sources. We pose event summarization as a clustering-based optimization problem and solve it using particle swarm optimization. The proposed methodology uses the search capability of particle swarm optimization, detecting the number of clusters automatically. Experiments are conducted with the Wikipedia Current Events Portal dataset and evaluated using the well-known ROUGE-1, ROUGE-2, and ROUGE-L scores. The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods. It attained improvement of 33.42%, 81.75%, and 57.58% in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.
%U https://aclanthology.org/2021.icon-main.54
%P 447-455
Markdown (Informal)
[Wikipedia Current Events Summarization using Particle Swarm Optimization](https://aclanthology.org/2021.icon-main.54) (Mishra et al., ICON 2021)
ACL