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.
Highlights
The technology of automatic document summarization is maturing and may provide a solution to the information overload problem
Document summarization plays an important role in information retrieval (IR)
Text summarization is the process of automatically creating a compressed version of a given text 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 [14]
Summary
The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Text summarization is the process of automatically creating a compressed version of a given text 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 [14]. Sentence based extractive summarization techniques are commonly used in automatic summarization to produce extractive summaries. Systems for extractive summarization are typically based on technique for sentence extraction, and attempt to identify the set of sentences that are most important for the overall understanding of a given document. In paper [11] proposed paragraph extraction from a document based on intra-document links between paragraphs It yields a text relationship map (TRM) from intra-links, which indicate that the linked texts are semantically related. 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
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