Back to Search
Start Over
Text Summarization Using FrameNet-Based Semantic Graph Model
- Source :
- Scientific Programming, Vol 2016 (2016)
- Publication Year :
- 2016
- Publisher :
- Hindawi Limited, 2016.
-
Abstract
- 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.
- Subjects :
- Word embedding
Article Subject
Exploit
Computer science
Text graph
02 engineering and technology
computer.software_genre
QA76.75-76.765
020204 information systems
Similarity (psychology)
0202 electrical engineering, electronic engineering, information engineering
Computer software
Information retrieval
business.industry
Rank (computer programming)
Automatic summarization
Computer Science Applications
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
020201 artificial intelligence & image processing
Artificial intelligence
FrameNet
business
computer
Software
Natural language processing
Sentence
Subjects
Details
- ISSN :
- 1875919X and 10589244
- Volume :
- 2016
- Database :
- OpenAIRE
- Journal :
- Scientific Programming
- Accession number :
- edsair.doi.dedup.....0aba6b8e798abd15868dca154d6927e5