AUTOMATIC TEXT SUMMARIZATION USING SUPPORT VECTOR MACHINE

被引：0

作者：

Begum, Nadira ^{[1
]}

Fattah, Mohamed Abdel ^{[1
,2
]}

Ren, Fuji ^{[1
,3
]}

机构：

[1] Univ Tokushima, Fac Engn, Tokushima 7708506, Japan

[2] Helwan Univ, FIE, Cairo, Egypt

[3] Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100088, Peoples R China

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2009年 / 5卷 / 07期

基金：

日本学术振兴会;

关键词：

Automatic summarization; Support vector machine; Text features; SENTENCE COMPRESSION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work investigates different text features to select the best one and proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train Support Vector Machine (SVM) in order to construct a text summarizer model. The proposed approach performance is measured at several compression rates (CR) on a data corpus composed of 100 English articles from the domain of politics.

引用

页码：1987 / 1996

页数：10

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