Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization
Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summ...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Institute Of Advanced Engineering And Science (IAES)
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27387/2/0130719062023242.PDF http://eprints.utem.edu.my/id/eprint/27387/ https://ijeecs.iaescore.com/index.php/IJEECS/article/view/30114 http://doi.org/10.11591/ijeecs.v30.i3.pp1795-1804 |
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| Summary: | Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims
to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and
sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of
ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text
summarization research. |
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