Multi-document summarization

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.

Abstract: : Event summarization is an effective process that mines and organizes event patterns to represent the original events. It allows the analysts to quickly gain the general idea of the events. In recent years, several event summarization algorithms have been proposed, but they all focus on how to find out the optimal summarization results, and are designed for one-time analysis. As event summarization is a comprehensive analysis work, merely handling this problem with a single optimal algorithm is not enough. In the absence of an integrated summarization solution, we propose an extensible framework META to enable analysts to easily and selectively extract and summarize events from different views with different resolutions. In this framework, we store the original events in a carefully-designed data structure that enables an efficient storage and multiresolution analysis. On top of the data model, we define a summarization language that includes a set of atomic operators to manipulate the meta-data. Furthermore, we present 5 commonly used summarization tasks, and show that all these tasks can be easily expressed by the language. Experimental evaluation on both real and synthetic datasets demonstrates the efficiency and effectiveness of our framework.

Abstract: For multi-document summarization where documents are collected over an extended period of time, the subject in a document changes over time. This paper focuses on subject shift and presents a method for extracting key paragraphs from documents that discuss the same event. Our extraction method uses the results of event tracking which starts from a few sample documents and finds all subsequent documents that discuss the same event. The method was tested on the TDT1 corpus, and the result shows the effectiveness of the method.