An Automatic Thai Text Summarization Using Topic Sensitive PageRank

The continuing growth of World Wide Web and on-line text collections makes a large volume of information available to users. Automatic text summarization allows users to quickly understand documents. In this paper, we propose an automated technique for single document summary extraction in Thai language which combines content-based and graph-based features and introduce the Topic Sensitive PageRank algorithm as a technique for ranking text segments. A series of experiments are performed using a Thai document collection. The results show the superiority of the proposed technique over reference systems

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