Extracting Main Content of a Topic on Online Social Network by Multi-document Summarization

Author

Chunyan Liu ; Conghui Zhu ; Tiejun Zhao ; Dequan Zheng

Author_Institution

MOE-MS Key Lab. of Natural Language Process. & Speech, Harbin Inst. of Technol., Harbin, China

fYear

2012

fDate

17-18 Nov. 2012

Firstpage

52

Lastpage

55

Abstract

Online social media has become one of the most important ways people communicate, while how to find valuable information from huge amounts of data becomes a key problem. We present a novel topic extraction method that employs topic value of each words and social model attributes as additional features based on the multi-document summarization. The experimental results show that the multi-document summarization with the topic and the sociality are helpful to extract topics from social media.

Keywords

Internet; document handling; information retrieval; social networking (online); main content extraction; multidocument summarization; online social media; online social network; social model attributes; topic extraction method; valuable information; Blogs; Data mining; Dictionaries; Feature extraction; Media; Natural language processing; Social network services; big data; media; multi-document summarization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2012 Eighth International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4673-4725-9

Type

conf

DOI

10.1109/CIS.2012.20

Filename

6407385