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Research Article | Open Access |

Improving Quality of Vietnamese Text Summarization Based on Sentence Compression

Author 1: Ha Nguyen Thi Thu Author 2: Tu Nguyen Ngoc Author 3: Cuong Nguyen Ngoc Author 4: Hiep Xuan Huynh
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 2 · Published 2016

DOI: 10.14569/IJACSA.2016.070250

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Abstract

Sentence compression is a valuable task in the framework of text summarization. In previous works, the sentence is reduced by removing redundant words or phrases from original sentence and tries to remain information. In this paper, we propose a new method that used Grid Model and dynamic programming to calculate n-grams for generating the best sentence compression. These reduced sentences are combined to text summarization. The experimental results showed that our method really effective and the text is grammatically, coherence and concise.

Keywords

Sentence compression topic modeling text summarization Grid model n-grams dynamic programming

How to Cite this Article

Thu, H. N. T., Ngoc, T. N., Ngoc, C. N., & Huynh, H. X. (2016). Improving Quality of Vietnamese Text Summarization Based on Sentence Compression. International Journal of Advanced Computer Science and Applications, 7(2). https://doi.org/10.14569/IJACSA.2016.070250

Thu, Ha Nguyen Thi, et al.. "Improving Quality of Vietnamese Text Summarization Based on Sentence Compression." International Journal of Advanced Computer Science and Applications, vol. 7, no. 2, 2016, https://doi.org/10.14569/IJACSA.2016.070250.

@article{Thu2016,
  title     = {Improving Quality of Vietnamese Text Summarization Based on Sentence Compression},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {2},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Ha Nguyen Thi Thu and Tu Nguyen Ngoc and Cuong Nguyen Ngoc and Hiep Xuan Huynh},
  doi       = {10.14569/IJACSA.2016.070250},
  url       = {https://doi.org/10.14569/IJACSA.2016.070250}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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Volume 17 No. 11, November 2026
Paper Submission Due October 25, 2026
Review Notification November 15, 2026
Publication Date November 30, 2026
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