Extractive text summarization using F-RBM

Sharma, B.R.; Tomer, Minakshi; Kriti, Kriti

doi:10.1080/09720510.2020.1808353

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The summary generated by fuzzy logic is then integrated with the summary generated by the Restricted Boltzmann Machine. The advantage of the FRBM model is that in dealing with noise during training, it is more resistant than RBM [21].…”

Section: Research Conducted Bymentioning

confidence: 99%

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

Widyassari¹,

Noersasongko²,

Syukur³

et al. 2022

IJACSA

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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.

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Section: Research Conducted Bymentioning

confidence: 99%

“…So in this feature, sentences with a lot of numerical data need to be considered to be included in the summary. The way to calculate it is to calculate the ratio of the number of numeric data in a sentence divided by the length of the sentence [21].…”

Section: 𝑆𝑐𝑜𝑟𝑒 (𝑆mentioning

confidence: 99%