Single Document Summarization Based on Local Topic Identification and Word Frequency

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.