In this study, the extractive summarization using sentence embeddings generated by the finetuned Bidirectional Encoder Representations from Transformers (BERT) models and the k-means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific data sets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the machine evaluation method Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and a human-based evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. The experimental results indicate that the BERT-based method can provide better and more informative summaries to engineers. It has also been demonstrated that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific data sets.