The exponential increase in publications presents a double-edged sword for researchers, offering unprecedented access to cutting-edge research and also burdening them with the task of navigating a huge landscape of literature. Due to this, automatic text summarization has garnered increasing prominence over the years, becoming pivotal in assisting researchers in navigating the vast landscape of academic literature. Numerous summarization techniques, including extractive, abstractive, extreme and explanatory approaches, have been explored in prior researches. This paper presents a comparative analysis among the abstractive and extractive summarization techniques to create a hybrid summarization model, culminating both the techniques. The selection of models is based on evaluation using ROUGE scores, ensuring the efficacy and robustness of hybrid approach in the context of research paper summarization.