Text summarizer and its Types !

Hello everyone!!

In this post I am going to explain how can we manage to summarize lengthy news , comprehensive reports and study notes using text summarizer. 

In in our busy schedule we don’t get time to read full newspaper. All we collect news by hearing summaries from other  people. But is not always possible to manually summarize the news or other text.There is an enormous amount of textual material, and it is only growing every single day.

      

Solution – Implementation of Text Summarizer using NLP.

This is where the awesome concept of Text Summarization using machine learning helped us out. It solves the one issue which kept bothering us before – now our model can understand the context of the entire text. It is dream come true for those who cant live without reading newspaper and for those students who study before exams.

Lets first understand what text summarization is before how it works. So here is the definition given .

"The ideal of automatic summarization work is to develop techniques by which a machine can generate summarize that successfully imitate summaries generated by human beings."

                          — Page 2, Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding, 2014.

Types of summarizer -
Text summarizer can be divided based on many factors.But here are two summarizers based on output of summarization.

Extraction based Summarization

The name gives away what this approach does. In this we identify what sentences are most important and show most of information.Those extracted sentences would be our summary. The below diagram illustrates extractive summarization:

Abstraction-based summarization

In this NLU as well as NLG both works. We try to generate new sentences from the original text bu understanding it. In this sentences are generated are  not actually present in the actual document. They are just summarized version of whole text. So it is more preferable than extraction based summarizer.

Thank you guys for reading !!
Stay tuned for my next article on how to implement this text summarizer.
If you have any doubts you can comment below.
                                                                                                         - By Ashwini Ghode