RVM optimization in automatic text summarization
K E Dewi, E Rainarli
Informatic Engineering Department, Universitas Komputer Indonesia, Jl. Dipatiukur 112-116 Bandung, Indonesia
Abstract
This study aims to optimize the RVM algorithm in automatic text summarization. This research began by studying various studies on automatic text summarization to find out what features are commonly used in the automatic text summarization process. Each feature value will be calculated as a correlation with the target. The composition of features is determined by the correlation value obtained, the greater the correlation value between features and targets, the feature will take precedence. The results in this study were obtained by using 4 or 6 features obtained the highest accuracy, which is 55.84%. This result is better than previous research. The conclusion of this study, accuracy can be improved by using the feature, where each feature is calculated correlation with the target.
Keywords: optimization; RVM; correlation; extraction features; automatic text summarization
Topic: Electrical and Computer Engineering