SUMMARIZATION BASED ON MULTIPLE FEATURE COMBINATION

被引：0

作者：

Huang, Taiwen ^{[1
]}

Li, Lei ^{[1
]}

Zhang, Yazhao ^{[1
]}

Chi, Junqi ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF 2016 4TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (IEEE CCIS 2016) | 2016年

关键词：

summarization; hLDA; feature combination; sentence scoring; sentence extraction;

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 paper presents the state of art research progress on Chinese single document summarization. Our method utilizes hLDA(hierarchical Latent Dirichlet Allocation) algorithm to model the documents first. A new feature is proposed from the hLDA modeling results, which can reflect some semantic information. Then it combines different traditional features to extract summary sentences from the documents. Four kinds of sentence extraction algorithms were designed to explore the effect of hLDA clusters. Our method attempts to find an optimal linear feature combination for sentence scoring through large scale experiments. After the comparison with other summarization methods, our method reveals better performance.

引用

页码：11 / 15

页数：5

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