Hallucinations significantly impact the practical use of abstractive summarization models, a problem previously attributed to the model's inability to generate accurate words. This study identifies an overlooked factor: the model's reckless syntactic structure planning, which accounts for most hallucinated content. Our findings suggest that the model inadequately considers the document’s available information when selecting syntactic structures. Instead, it erroneously and directly replicates the co-occurrence relationship between document fragments and summary syntactic structures in the training data, which leads to the inclusion of hallucinated content. To address this issue, we propose a new method, named TSSP, that guides the model to plan more targeted syntactic structures. The experimental results demonstrate a 90.4% reduction in hallucinated content and a 17.6% improvement in factual consistency (as evaluated by FactCC) compared to the strongest baselines.