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Traditional seq2seq model didn' t fully utilize the sentence structure of the input text, this results in that seq2seq model can't generate a text summary with rigorous structure and better grammar. Therefore, the paper proposed a summary generation method with basic structure of sentence. First of all, it explicitly proposed the concept of generating a text summary with sentence structure. Furthermore, it added a sentence structure module and a supervisor module on the summary generator. The sentence structure module captured a series of sentence structures from the input text, the supervisor module evaluated the importance of the sentence structures, this assisted the sentence structure module to capture some available sentence structures. Finally, the summary generator combined the available sentence structures to generate the final summary. The experiments on LCSTS show that its ROUGE scores are higher than seq2seq. In other words,its accuracy surpasses seq2seq. Experiments also verify the necessity of the existence of the sentence structure module and the supervisor module. [ABSTRACT FROM AUTHOR]