Owing to the proliferation of internet and multimedia applications, there has been a huge increase in digital content in the form of text, images, audio and video worldwide. The various applications of text summarization has attracted the computer vision researchers to generate the optimal text summaries. Several methodologies have been reported in literature to generate text summaries. However, the key challenge is incorporating user’s preference as text summarization is a subjective task. User generated queries act as a guiding beacon in the summarization process. Instead of producing generic text summaries, query-based text summarization techniques offer user preferred responses. This paper begins by discussing the fundamental concepts and objectives of text summarization, emphasizing on the role of user queries in the summarization process. It proceeds to categorize existing query-based summarization techniques into distinct paradigms, including extractive, abstractive, and hybrid approaches, highlighting the advantages and limitations of each. This paper presents the evolution of query-based text summarization with developed techniques, available datasets, evaluation metrics, and performance comparison. After conducting a survey on query-based text summarization, a text summarization using the TextRank algorithm is implemented and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores are calculated for the generated summaries.