In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful.We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges. Index Terms: Summarization, Graph based summarization, Meta-heuristic based summarization, Maximal marginal relevance based summarization, Evaluation

Automatic Text Summarization is one of the most trending research areas in the field of Natural Language Processing. The main aim of text summarization is to reduce the size of a text without losing any important information. Various techniques can be used for automatic summarization of text. In this paper we are going to focus on the automatic summarization of text using graph-based methods. In particular, we are going to discuss the implementation of a general-purpose web application which performs automatic summarization on the text entered using the Text Rank Algorithm. Summarization of text using graph-based approaches involves pre-processing and cleansing of text, tokenizing the sentences present in the text, representing the tokenized text in the form of numerical vectors, creating a similarity matrix which shows the semantic similarity between different sentences present in the text, representing the similarity matrix as a graph, scoring and ranking the sentences and extracting the summary. Keywords: Text Summarization, Unsupervised Learning, Text Rank, Page Rank, Web Application, Graph Based Summarization, Extractive Summarization