An Efficient Vietnamese Text Summarization Approach Based on Graph Model

This paper proposes an automatic method to generate an extractive summary of multiple Vietnamese documents which are related to a common topic by modeling text documents as weighted undirected graphs. It initially builds undirected graphs with vertices representing the sentences of documents and edg...

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Bibliographic Details
Main Authors: Tu Anh Nguyen Hoang, Hoang Khai Nguyen, Quang Vinh Tran
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data mining
Lead
Machine learning
Meteorology
Pragmatics
Redundancy
Citations: Items that cite this one
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Summary:This paper proposes an automatic method to generate an extractive summary of multiple Vietnamese documents which are related to a common topic by modeling text documents as weighted undirected graphs. It initially builds undirected graphs with vertices representing the sentences of documents and edges indicate the similarity between sentences. Then, by adopting PageRank algorithm, we can generate salient scores for sentences. Sentences are ranked according to their salient scores and selected based on Maximal marginal relevance to form the summaries. These summaries are combined and applied the same process one more time to form the final extractive summary of the document set. A series of experiments are performed on Vietnamese news articles. The results demonstrate the effectiveness of the proposed technique over reference systems.
DOI:10.1109/RIVF.2010.5633162