Efficient keyword extraction for meaningful document perception
Proceedings of the 11th ACM symposium on Document engineering
Text summarisation in progress: a literature review
Artificial Intelligence Review
Integer linear programming for dutch sentence compression
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Automatic Organization and Generation of Presentation Slides for E-Learning
International Journal of Distance Education Technologies
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In this task, an approach for single document summaries based on local topic identification and word frequency is proposed. In recent years, there has been increased interest in automatic summarization. The physical features are often used and have been successfully applied to this field; it also has some disadvantages of non-redundancy, structure and coherence. Therefore, we introduced logical structure feature which has been successfully applied in multi-document summarization (MDS), and we designed a system to accomplish this task. Documents can be clustered into local topic after sentences similarity is calculated, which can be sorted by the scoring. Then sentences from all local topics are selected by computing the word frequency. Using this proposed method, the information redundancy of each local topic and among local topic is reduced. The information coverage ratio and structure of the summarization is improved.