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Undergraduate Theses from gdlhub / 2020-01-09 11:54:49
Oleh : Aris Fanani, Yuniar Farida, Putra Prima Arhandi, M. Mahaputra Hidayat, Abdul Muhid, Billy Montolalu, Telkomnika
Dibuat : 2020-01-09, dengan 1 file

Keyword : s: multi-document summarization, sentence position, support vector regresion
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/issue/view/640
Sumber pengambilan dokumen : web

Document summarization is needed to get the information effectively and efficiently. One method

used to obtain the document summarization by applying machine learning techniques. This paper

proposes the application of regression models to query-focused multi-document summarization based on

the significance of the sentence position. The method used is the Support Vector Regression (SVR) which

estimates the weight of the sentence on a set of documents to be made as a summary based on sentence

feature which has been defined previously. A series of evaluations performed on a data set of DUC 2005.

From the test results obtained summary which has an average precision and recall values of 0.0580 and

0.0590 for measurements using ROUGE-2, ROUGE 0.0997 and 0.1019 for measurements using

the proposed regression-SU4. Model can perform measurements of the significance of the position of

the sentence in the document well.

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