Abstract
Automatic summarization is a hot research topic in the field of natural language processing. Now the automatic summarization technology is mostly for majority languages such as English and Chinese, while less for the rare languages such as Indonesian. We aim to analyze the development trends of summarization and explore the processing methods for Indonesian summarization. This paper introduces the implementation process and experimental results of Indonesian text summarization based on the keyword frequency extraction method. The experimentation compares the RUGOE-2 result of our keyword-based system with that of the PSKSUMSUM one in Indonesian text summarization. The experimental results show that the keyword-based Indonesian text summarization is more effective.
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References
Barzilay, R.: Text Summarization. MIT (2005)
Radev, D.R., Fan, W., Zhang, Z.: WebInEssence: A Personalize Web-Based Multi-Document Summarization and Recommendation System (2001)
Radev, D.R., Fan, W.: Automatic summarization of search engine hit lists. In: Proceeding, ACL Workshop on Recent Advances in NLP and IR, Hong Kong, October
Saggion, H., Gaizauskas, R.: Multi-document summarization by cluster profile relevance and redundancy removal (2004)
Evans, D.K., Klavans, J.L.: Columbia Newsblaster: Multilingual News Summarization on the Web (2004)
Lloret, E.: Text Summarization: An Overview (2008)
Lu, D., Pan, X., Pourdamghani, N., Chang, S.-F., Ji, H., Knight, K.: A Multi-media Approach to Cross-lingual Entity Knowledge Transfer. Computer Science Department, Rensselaer Polytechnic Institute, Information Sciences Institute, University of Southern California, Electrical Engineering Department, Columbia University (2016)
Zhang, J., Wang, T., Wan, X.: PKUSUMSUM: A Java Platform for Multilingual Document Summarization. Institute of Computer Science and Technology, Peking University, The MOE Key Laboratory of Computational Linguistic, Peking University (2016)
Yao, J.G., Wan, X., Xiao, J.: Recent Advances in Document Summarization (2017)
He, Z., Chen, C., Bu, J., Wang, C., Zhang, L., Cai, D., He, X.: Document summarization based on data reconstruction. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
Acknowledgements
The research is supported by the Key Project of State Language Commission of China (No. ZDI135-26), the Natural Science Foundation of Guangdong Province (No. 2018A030313672), the Featured Innovation Project of Guangdong Province (No. 2015KTSCX035), the Bidding Project of Guangdong Provincial Key Laboratory of Philosophy and Social Sciences (No. LEC2017WTKT002), and the Key Project of Guangzhou Key Research Base of Humanities and Social Sciences: Guangzhou Center for Innovative Communication in International Cities (No. 2017-IC-02).
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Liu, J., Liu, W. (2020). Keyword-Based Indonesian Text Summarization. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_6
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DOI: https://doi.org/10.1007/978-981-13-9406-5_6
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