In recent years, the rise of multimedia content on the internet has inundated users with a vast and diverse array of information, including images, videos, and textual data. Handling this flood of multimedia data necessitates advanced techniques capable of distilling this wealth of information into concise, meaningful summaries. Multimodal summarization, which involves generating summaries from multiple modalities such as text, images, and videos, has become a pivotal area of research in natural language processing, computer vision, and multimedia analysis. This survey paper offers an overview of the state-of-the-art techniques, methodologies, and challenges in the domain of multimodal summarization. We highlight the interdisciplinary advancements made in this field specifically on the lines of two main frontiers:1) Multimodal Abstractive Summarization, and 2) Pre-training Language Models in Multimodal Summarization. By synthesizing insights from existing research, we aim to provide a holistic understanding of multimodal summarization techniques.