Multi-document summarization systems comparison

Author

Lei Li ; Wei Heng ; Ping´an Liu

Author_Institution

Sch. of Comput. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China

Volume

03

fYear

2012

fDate

Oct. 30 2012-Nov. 1 2012

Firstpage

1409

Lastpage

1413

Abstract

This paper compared two multi-document summarization systems we developed. One system used hierarchical sentence clustering algorithm to find the important information, while the other system mainly adopted hierarchical Latent Dirichlet Allocation (hLDA) topic model to obtain the sub-topics of multi-document data. Both of the two systems are evaluated and compared on TAC 2010/TAC 2011 data using the ROUGE testing method with same parameters´ setting. The results have shown that the hLDA system has got some improvement compared with the clustering system. And normally in ROUGE testing, results from non-stopwords are better than those from stopwords.

Keywords

document handling; pattern clustering; ROUGE testing method; hLDA; hierarchical Latent Dirichlet Allocation; hierarchical sentence clustering algorithm; multidocument data; multidocument summarization systems comparison; Clustering algorithms; Computational modeling; Feature extraction; Probabilistic logic; Resource management; Semantics; Vectors; HLDA; hierarchical sentence clustering; multi-document summarization; system comparison;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-1855-6

Type

conf

DOI

10.1109/CCIS.2012.6664617

Filename

6664617