Analysis of Different Approaches for Automatic Text Summarization

In the NLP culture, text summarization is regarded as a difficult task. We now have access to a vast amount of information from offline and online resources. We may find many papers on a single topic, each offering a wealth of knowledge. Manually extracting valuable information from them is really tough. Automatic text summarizing techniques have been developed to address this issue. Text summarizing is the process of taking relevant information from huge papers and condensing it into a short summary that retains all of the important information. This survey study provides a general overview of the work that has been implemented with topic of automatic text summarizing in different languages and utilizing several text summarization methodologies. Text summary is the method of defining and compressing the most significant meaningful material in a document or collection of similar documents into a shorter version that retains its general meanings. This paper aims to discuss various techniques and approaches used by automated text summarization researchers.