For overcoming the drawback of ignoring the semantic relationship between terms and concept structure in the bag of words model, a source code summarization technology based on syntactic analysis was proposed. Firstly, the part-of-speech tagging was utilized to recognize the keywords that characterized the code feature most. Secondly, the chunk parsing was used to revise the errors that could be introduced in the process of part-of-speech tagging. Thirdly, the noise reduction for those keywords was carried out to decrease the influence of text noise. Finally, several keywords with highest weights were selected to compose the summaries. Through the comparison with TF-IDF (Term Frequency-Inverse Document Frequency)-based and extended TF-IDF-based source code summarization technologies in the experiment, with respect to the overlap coefficient of the golden set, the summaries obtained by the proposed technology are improved by at least 9% and 6% respectively, which illuminates that the proposed technology is able to generate more precise source code summaries.