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Scientific Programming
Volume 2016, Article ID 5130603, 10 pages
Research Article

Text Summarization Using FrameNet-Based Semantic Graph Model

1School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2Key Laboratory of Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing 100876, China
3Department of Statistics, Harvard University, Cambridge, MA, USA
4Air Force General Hospital, Beijing, China

Received 8 August 2016; Accepted 30 October 2016

Academic Editor: Xiong Luo

Copyright © 2016 Xu Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Text summarization is to generate a condensed version of the original document. The major issues for text summarization are eliminating redundant information, identifying important difference among documents, and recovering the informative content. This paper proposes a Semantic Graph Model which exploits the semantic information of sentence using FSGM. FSGM treats sentences as vertexes while the semantic relationship as the edges. It uses FrameNet and word embedding to calculate the similarity of sentences. This method assigns weight to both sentence nodes and edges. After all, it proposes an improved method to rank these sentences, considering both internal and external information. The experimental results show that the applicability of the model to summarize text is feasible and effective.