Automatic summarization of text in the biomedical field includes the usage of natural language processing (NLP) and machine learning techniques to generate concise and coherent summaries of biomedical texts. The paper present a Biomedical text abstractive summarization tool that uses Stacked LSTM(Long Short Term Memory) including attention layer and copying mechanism. Also, the model uses UMLS(Unified Medical Language System) for better learning of medical terms so as to adapt the domain knowledge which plays very important role for preparing summary for domain specific text. Data is first pre-processed which involves removing irrelevant text and stop words and normalizing the text. Medical Vocabulary is added with the help of UMLS which is a large Biomedical Metathesarus. It is then passed to the seq2seq model i.e. the stacked LSTM.The encoder uses three layers of LSTM and single LSTM layer for decoder. The role is to convert the preprocessed text into word embedding which are then passed to the Attention layer and hence allows the Attention layer to identify the most crucial parts of the original text and the database documents. The pointer network uses the contextualized representation to generate a condensed representation of the database documents which further generates summary i.e. concise and informative. Earlier work is using only single evaluation metric while the highlight of our work is that the prepared summary is evaluated two metrics i.e. BLEU score and ROUGE score and has achieved 0.921 and 59.9% respectively which is slightly higher than the existing work.