Open AccessRelevance Vector Machine Optimization in Automatic Text Summarization
Author(s) -
Kania Evita Dewi,
Ednawati Rainarli
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/662/5/052003
Subject(s) - automatic summarization , relevance (law) , computer science , relevance vector machine , correlation , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , text graph , support vector machine , correlation coefficient , feature extraction , value (mathematics) , feature vector , natural language processing , data mining , machine learning , mathematics , linguistics , philosophy , geometry , political science , law
This study aims at optimizing the Relevance Vector Machine (RVM) algorithm in automatic text summarization. This research begins 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 target. The composition of features is determined by obtained correlation value, when correlation value between features and targets is grater, the feature will take precedence. The results in this study are obtained by using 4 or 6 features that obtains highest accuracy, which is 55.84%. The conclusion of this study is that the correlation coefficient can be used to determine the order of extraction features.