Text Summarization with Semantics Information

Due to the wide use of Internet and the diversity of information, there is a large amount of information which is available to users. So many techniques have been developed for the access of large amount data quickly and accurately. Text summarization helps in reducing the size of a text while preserving its information content. One of the main drawbacks of Automatic Summarization is the vague semantic classification of the document, which results in the poor quality of the consequent summaries. In this paper, we propose an automatic summarization approach utilizing both Semantics and Text-Rank algorithm. Semantic graph-based approach is used for extractive summarization in order to solve the problem. The summarizer uses WordNet to produce a semantic graph that represents the document in such a way that edges between sentences are based on semantic similarity between sentences and the sentences are ranked by applying PageRank to the resulting graph. A summary is formed by selecting the top ranked sentences, using a threshold based on required size of the summary. ROUGE toolkit is used for evaluation of summaries DUC 2002 datasets and generates results.