Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text ...

Text summarization is basically summarizing of the given paragraph with use natural language processing and machine learning. There has been an explosion in quantity textual content records from lot sources. This a useful supply facts information which needs to be efficiently summarized useful. In this paper, primary tactics computerized were described. The distinctive approaches for effectiven...

Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness readability. These systems consider extracted as only input, which is relatively focused but lose important background knowledge. In this paper, we investigate contextualized rewriting, ingests entire original document...

With the proliferation of online information, text summarization has become essential to provide enhanced mechanisms to perceive and present effective textual information. It is very difficult for human beings to manually summarize large documents of text. Automatic text summarization has become an important and timely tool for assisting and interpreting text information. Text summarization is ...