Manual text summarization for large quantities of text files is time consuming, costly and re- quires a lot of efforts. Which can be a tiresome and exhausting process. Automatic Text Summarization(ATS) is becoming more and more important lately due to the huge amounts of textual content created on the internet on a daily basis and keeps growing exponentially. ATS can be classified into two main approaches extractive approach, and abstractive approach. The extractive approach selects the most important sentences from the original text and combines them to form a comprehensive summary without changing the words. The abstractive approach from the original text generates new sentences with different words to convey the meaning in a shorter form. This project focus on the extractive approach and proposed model for text summarization using two summary types: Full Summary and Highlight Summary. The model realized as a tool that contained the two summary types.Based on our results, the summaries generated by our model were a short paragraph and few sentences in bullet point form sum- marizing the original text. The proposed model use the standard Document Understanding Conference 2001 dataset to evaluated by using evaluation tool: Recall-Oriented Understudy for Gisting Evaluation (ROUGE ) and compared with a manual summary for both models. Were the evaluation results show that the proposed model attain the highest score on ROUGE-1 recall metric ”0.5027 ” for Full summary, and for Highlight summary gets ROUGE-1 precision metric ”0.7007”.