Paper Title
Comparison of Extractive Text Summarization Methods

Extractive Text Summarization is a NLP problem of forming a summary by using the most important sentences of an article and has been addressed in many different ways. In our research, we develop a model that uses metrics for giving importance to sentences, and apply it to Machine Learning and Deep Learning Models. Using the results obtained we find the optimal approach, along with other approaches that work particularly well, to do extractive text summarization. In addition, we analyze why these models perform better than the rest. Finally, we also find the importance of each metric in forming a summary based on several evaluation measures. Keywords - Natural Language Processing, Extractive Text Summarization, Deep Learning.