What is Text Summarization in NLP?

“Automatic text summarization is the task of producing a concise and fluent summary while preserving key information content and overall meaning”

                                 -Text Summarization Techniques: A Brief Survey, 2017

There are broadly two different approaches that are used for text summarization:

1.Extractive Summarization

2.Abstractive Summarization

Extractive Summarization

The name gives away what this approach does. We identify the important sentences or phrases from the original text and extract only those from the text. Those extracted sentences would be our summary. The below diagram illustrates extractive summarization: text summarization

Abstractive Summarization

This is a very interesting approach. Here, we generate new sentences from the original text. This is in contrast to the extractive approach we saw earlier where we used only the sentences that were present. The sentences generated through abstractive summarization might not be present in the original text:

In this project, we aredoing Extractive Text Summarization using Text Tank Algorithm. We will then go ahead and Implement Keras Embedding Layer to get Sentence Weights and there by creating a summary based on the Weights of the sentences

For In detail Explaination regarding the Text Rank Algorithm, please go through the Word File Text Summarization Methods

Word Embedding is an essential component in any kind of text Analysis. Be it Sentiment analysis or Text Summarization. Here we will be using this for text summarization.

For in detail Explaination about Embedding Layer in Keras, please go through the word document Word Embedding using Keras

The DataSet used for this project can be found here - https://drive.google.com/file/d/1HPShiXSrHMNlfcMZn-WFYjoftitOH9fJ/view?usp=sharing