Exploring strategies for developing link analysis based question-oriented multi-document summarization models

Author

Li, Su-Jian ; Wang, Wei ; Li, Wen-Jie

Author_Institution

Key Lab. of Comput. Linguistics, Peking Univ., Beijing, China

Volume

4

fYear

2011

fDate

10-13 July 2011

Firstpage

1896

Lastpage

1901

Abstract

Graph ranking algorithms have been successfully used in multi-document summarization. Among them, the basic link analysis model has drawn much attention due to its´ mutual reinforcement principle which appears to be sound for the generic summarization task. In this paper, we explore effective strategies for extending the basic link analysis model to question-oriented multi-document summarization. Three kinds of strategies, namely link re-weighting, baseset downsizing and projection, are proposed to introduce question-dependent similarity metric, adjust the node number and refine the ranking process respectively. Experimental results evaluated on the DUC data sets demonstrate that these three strategies can achieve better results.

Keywords

Internet; document handling; graph theory; Internet; baseset downsizing; exploring strategies; generic summarization; graph ranking algorithms; link analysis based question oriented multidocument summarization model development; node number; ranking process; reinforcement principle; Algorithm design and analysis; Analytical models; Biological system modeling; Computational modeling; Cybernetics; Machine learning; Measurement; Baseset Downsizing; Link Analysis Model; Link Re-weighting; Projection; Question-oriented Multi-document Summarization;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016951

Filename

6016951