MSBGA: A Multi-Document Summarization System Based on Genetic Algorithm

Author

He, Yan-Xiang ; Liu, De-xi ; Ji, Dong-hong ; Yang, Hua ; Teng, Chong

Author_Institution

Sch. of Comput., Wuhan Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2659

Lastpage

2664

Abstract

The multi-document summarizer using genetic algorithm-based sentence extraction (MSBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, MSBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. The evaluation function employs four features according to the criteria of a good summary: satisfied length, high coverage, high informativeness and low redundancy. To improve the accuracy of term frequency, MSBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04

Keywords

abstracting; genetic algorithms; text analysis; NP hard problem; genetic algorithm; multidocument summarization system; optimization; sentence extraction; term frequency; word sense; Clustering algorithms; Clustering methods; Coherence; Cybernetics; Data mining; Frequency; Fusion power generation; Genetic algorithms; Helium; Machine learning; Physics computing; Tides; MSBGA; Multi-document summarization; genetic algorithm; term frequency with sense (TFS);

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258921

Filename

4028512