Abstract
In recent years, with the increase of available digital information on the Web, the time needed to find relevant information is also increased. Therefore, to reduce the time spent on searching, research on automatic text summarization has gained importance. The proposed summarization process is based on event extraction methods and is called an event-based extractive single-document summarization. In this method, the important features of event extraction and summarization methods are analyzed and combined together to extract the summaries from single-source news documents. Among the tested features, six features are found to be the most effective in constructing good summaries. The constructed summaries are tested on benchmark Document Understanding Conferences 2001 and 2002 datasets, and the results outperformed most of the other well-known summarization methods.
Highlights
Advances in information technology enable us to share enormous amount of data on the web and its applications
In 2000, the National Institute of Standards and Technology (NIST) introduced the Document Understanding Conferences (DUCs) [1] to encourage researchers to work on text summarization
The proposed EBDS system outperformed the other methods in the R-2 measure while falling behind COSUM and RL-Full in the R-1 measure on both datasets
Summary
Advances in information technology enable us to share enormous amount of data on the web and its applications. Managing and using vast amounts of data for the needs of users becomes an important research area. Automatic document summarization is used to extract the main idea of the document by eliminating the less significant and redundant parts of the information. Depending on the number of documents it processes, automatic summarization is categorized as single or multidocument summarization. In 2000, the National Institute of Standards and Technology (NIST) introduced the Document Understanding Conferences (DUCs) [1] to encourage researchers to work on text summarization. In 2002, the DUC stopped supporting research on single-document summarization, considering that it is harder to extract summaries. Most of the research has focused on multidocument summarization
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