A new graph based text segmentation using Wikipedia for automatic text summarization
Citations Over TimeTop 20% of 2012 papers
Abstract
The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. According to the input text, in this paper we use the knowledge base of Wikipedia and the words of the main text to create independent graphs. We will then determine the important of graphs. Then we are specified importance of graph and sentences that have topics with high importance. Finally, we extract sentences with high importance. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to state-of-the-art summarization approaches.
Related Papers
- → An overview of Text Summarization techniques(2016)91 cited
- → Literature Study on Multi-document Text Summarization Techniques(2016)14 cited
- → Automatic Text Summarization(2021)10 cited
- → Application of Graph Neural Network in Automatic Text Summarization(2021)5 cited
- → Introducing Word's Importance Level-Based Text Summarization Using Tree Structure(2019)1 cited