Text Summarizer
A text summarizer with Summarizing Tool is an online tool that wraps up a text to a specified short length. It condenses a long article to main points.
The need for text summarizers are increasing day by day, because of time constraints.
People are looking for shortcut methods to learn ideas in lesser time. Even text summarizers are helping them to decide whether a book, a research paper, or an article is worth reading or not.
Oxford defines summary as:
“a short statement that gives only the main points of something, not the details.”
Approaches in auto summarization:
Mainly two approaches have been developed over time for summarizing a long text into a shorter one.
Extraction Summarization:
This approach entails the method to extract keywords and phrases from sentences and then join them to produce a compact meaningful summary.
Abstractive Summarization:
In this method, algorithms are developed in such a way to reproduce a long text into a shorter one by NLP. It retains its meaning but changes the structure of sentences.
How does this text summarizer work?
Trained by machine learning, paraphraser.io text summarizer uses the concept of abstractive summarization to summarize a book, an article, or a research paper.
It uses NLP to create novel sentences and generates a summary in which the main idea remains intact. IT is an advanced-level tool that uses AI for its work. Therefore, the summary produced by this tool appears to be flawless and inflow.
How to use our text summarizer?
Our summarizing tool is the best because it is simple to use and efficient also.
Insert the text (article, research paper, book extract) into the text area.
Or upload your content.
Click the “Summarize” Button.
You can also toggle other features by selecting show bullets, best line, ranked base, and summary length.
Important features of this text summarizer:
The features that give this text summarizer an advantage over others are given below.
Control summarization:
This is the best feature of this tool because it gives you the freedom to choose the length of your summarized text.
It depends upon the circumstances, sometimes you want to create a long summary and sometimes a shorter one is enough. This tool gives you the choice to summarize your text according to your needs.
Bullet points formation:
When you want to analyze your text, you can use our text summarizer to create bullet points. This can help you in creating PowerPoint slides and presentations.
Rating of the text:
It’s a full pack feature that gives you the whole ranking of your text. It provides the best line, best sentence, and general ranking of your text according to its optimization.
Free usage:
Our text summarizer has free usage and can be used whenever it is needed. You can instantly use it without giving any login.
Users of text summarizer:
Students:
A text summarizer helps students to condense difficult concepts by summarizing them. They get the know-how of complex articles and books. Moreover, manual summarizing can be very time-consuming. They use a text summarizer to solve their assignments in lesser time.
Journalists:
Journalists can get help from our text summarizer as they have to communicate an incident or an event. Giving thorough news is not valuable as compared to quick headliners. So, they can use this summarizing tool to inform people about daily happenings.
Writers:
Writers often have to face the difficulty of creating unique content either blogs or guest posts. They can only produce exceptional content if they know the gist of the whole story.While getting ideas from different sources, they can use our text summarizer to skim out the necessary information. This information is incorporated into what they are writing.
What’s the need for text summarization?
Propelled by the modern technological innovations, data is to this century what oil was to the previous one. Today, our world is parachuted by the gathering and dissemination of huge amounts of data.
In fact, the International Data Corporation (IDC) projects that the total amount of digital data circulating annually around the world would sprout from 4.4 zettabytes in 2013 to hit 180 zettabytes in 2025. That’s a lot of data!
With such a big amount of data circulating in the digital space, there is need to develop machine learning algorithms that can automatically shorten longer texts and deliver accurate summaries that can fluently pass the intended messages.
Furthermore, applying text summarization reduces reading time, accelerates the process of researching for information, and increases the amount of information that can fit in an area.
What are the main approaches to automatic summarization?
There are two main types of how to summarize text in NLP:
Extraction-based summarization
The extractive text summarization technique involves pulling keyphrases from the source document and combining them to make a summary. The extraction is made according to the defined metric without making any changes to the texts.
Here is an example:
Source text: Joseph and Mary rode on a donkey to attend the annual event in Jerusalem. In the city, Mary gave birth to a child named Jesus.
Extractive summary: Joseph and Mary attend event Jerusalem. Mary birth Jesus.
As you can see above, the words in bold have been extracted and joined to create a summary — although sometimes the summary can be grammatically strange.
Abstraction-based summarization
The abstraction technique entails paraphrasing and shortening parts of the source document. When abstraction is applied for text summarization in deep learning problems, it can overcome the grammar inconsistencies of the extractive method.
The abstractive text summarization algorithms create new phrases and sentences that relay the most useful information from the original text — just like humans do.
Therefore, abstraction performs better than extraction. However, the text summarization algorithms required to do abstraction are more difficult to develop; that’s why the use of extraction is still popular.
Here is an example:
Abstractive summary: Joseph and Mary came to Jerusalem where Jesus was born.
How does a text summarization algorithm work?
Usually, text summarization in NLP is treated as a supervised machine learning problem (where future outcomes are predicted based on provided data).
Typically, here is how using the extraction-based approach to summarize texts can work:
1. Introduce a method to extract the merited keyphrases from the source document. For example, you can use part-of-speech tagging, words sequences, or other linguistic patterns to identify the keyphrases.
2. Gather text documents with positively-labeled keyphrases. The keyphrases should be compatible to the stipulated extraction technique. To increase accuracy, you can also create negatively-labeled keyphrases.
3. Train a binary machine learning classifier to make the text summarization. Some of the features you can use include:
Length of the keyphrase
Frequency of the keyphrase
The most recurring word in the keyphrase
Number of characters in the keyphrase
4. Finally, in the test phrase, create all the keyphrase words and sentences and carry out classification for them.
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