Automatic code summarization techniques aid in program comprehension as they try to generate a human-level summary in natural language from a programming language. Recent research in this area has seen significant developments from basic Seq2Seq models to different flavors of Transformer models, which try to encode the structural components of the source code using some input representation. Apart from the source code itself, other components, such as API knowledge, have previously been helpful in code summarization using recurrent neural networks (RNN) as it gives crucial information about the code’s functionality. So, in this article, along with the source code structure, we explore the importance of API knowledge in code summarization and try to understand whether it helps in improving the summaries. Our model uses a Transformer-based architecture containing two encoders for two input modules, source code and API sequences, and a joint decoder to generate summaries combining the outputs of two encoders. We experimented with our proposed model on a dataset of java projects collected from GitHub containing around 87K <Java Method, API Sequence, Comment> triplets. The experiments show our model outperforms most of the existing RNN-based approaches, but the overall performance does not improve compared with the state-of-the-art approach using Transformers. Thus, the results show that although API information is helpful for code summarization, we still need better methods to extract the valuable information from the API sequences.