Paper Info
Title
Mutual-reinforcement document summarization using embedded graph based sentence clustering for storytelling
Abstract
In this paper, a document summarization framework for storytelling is proposed to extract essential sentences from a document by exploiting the mutual effects between terms, sentences and clusters. There are three phrases in the framework: document modeling, sentence clustering and sentence ranking. The story document is modeled by a weighted graph with vertexes that represent sentences of the document. The sentences are clustered into different groups to find the latent topics in the story. To alleviate the influence of unrelated sentences in clustering, an embedding process is employed to optimize the document model. The sentences are then ranked according to the mutual effect between terms, sentence as well as clusters, and high-ranked sentences are selected to comprise the summarization of the document. The experimental results on the Document Understanding Conference (DUC) data sets demonstrate the effectiveness of the proposed method in document summarization. The results also show that the embedding process for sentence clustering render the system more robust with respect to different cluster numbers.
Year
DOI
Venue
2012
10.1016/j.ipm.2011.12.006
Inf. Process. Manage.
Keywords
Field
DocType
document model,document summarization framework,mutual-reinforcement document summarization,high-ranked sentence,document summarization,sentence clustering,story document,embedded graph,document modeling,embedding process,essential sentence,mutual effect
Automatic summarization,Storytelling,Data set,Information retrieval,Ranking,Document clustering,Computer science,Document summarization,Natural language processing,Artificial intelligence,Cluster analysis,Sentence
Journal
Volume
Issue
ISSN
48
4
0306-4573
Citations 
PageRank 
References 
4
0.39
29
Authors
3
Name
Order
Citations
PageRank
Zhengchen Zhang1776.29
Shuzhi Sam Ge27786444.41
Hongsheng He3537.40