It is very important to use automatic text summarization to turn long chunks of text into short, useful snippets. In the area of automatic text summary, this study looks at both semantic and syntactic methods in great detail. Using methods like natural language understanding and semantic processing, semantic approaches try to get meaning and context from writing. These methods try to get at the main ideas and connections in the material so that recaps can be more complex and full of information. Syntactic methods, on the other hand, focus on the structure of language, such as grammar and syntax. To find important sentences and phrases for summarization, methods like sentence extraction based on grammar patterns and grammatical analysis are used. Syntactic methods often make sure that the outlines they create are grammatically right and make sense. This review compares both methods in a number of areas, such as how well they capture the spirit of the original text, how well the generated snippets make sense, how quickly they can be produced, and how well they can be used with different types of material. The pros and cons of each method are shown in comparative studies, which help us understand how they can be used in various areas and situations. Recent improvements that combine semantic and syntactic methods have also shown promise in making automatic recaps better overall and more helpful. The goal of these mixed methods is to make reports that are both verbally and semantically correct by using the benefits of both semantic understanding and syntactic structure. This review gives us a more complete picture of how automatic text summarization methods are changing. It stresses how important it is to look at both semantic and syntactic approaches in order to make the field move forward and create better and more advanced summary systems.