Information Extraction deals with the automated extraction of knowledge from unstructured sources. This field unfolded new avenues for querying, organizing, and analyzing information by drawing clean linguistics of structured information and also the abundance of unstructured data. This study aimed to differentiate the assorted styles of informational text structure within the text information. Classification of informational text structure during a given text is a vital space of analysis for locating data within the text content. Several previous studies outlined a collection of classes of informational text structures that identify respective signal words. This paper proposes automatic extraction of text informational structure from various sources with extracting techniques like extractive summarization, abstractive summarization, name entity recognition, event extraction, and question answering which is useful to the reader to know the information exactly. It makes effective for the various steps inclined in information extraction adapting to dynamic data, integrating with existing entities, and handling uncertainty in the existing process.

Text structure, Signal word, Extractive Summarization, Abstractive Summarization, Name Entity Recognition, Event Extraction, Question Answering