An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

This study aims to predict candidate summary sentences in extractive summary using the Fuzzy-Decision Tree method. The fuzzy method is quite superior and the most widely used in extractive summaries, because Fuzzy has advantages in calculations that are not cryptic, so it is able to calculate uncertain possibilities. However, in its implementation, the fuzzy rule generation process is often carried out randomly or based on expert understanding so that it does not represent the distribution of the data. Therefore, in this study, a Decision Tree (DT) technique was added to generate fuzzy rules. From the fuzzy final result, important sentences are obtained that are candidates for summary sentences. The performance of our proposed method was tested on the 2002 DUC dataset in the ROUGE-1 evaluation. The results showed that our method outperformed other methods (baseline and sentence ranking) with an average precision of 0.882498, Recall 0.820443 and F Measure 0.882498 with CI for F1 0.821-0.879 at the 95% confidence level.

@article{Widyassari2022,
  title     = {An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Adhika Pramita Widyassari and Edy Noersasongko and Abdul Syukur and Affandy},
  doi       = {10.14569/IJACSA.2022.0130768},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130768}
}