In the last two decades, world has generated a lot of data online as well as offline. However, not everyone can read the entire document. Sometimes, you might want to go through the summary of the long content. In such a scenario, Text Summarization seems very helpful so that you can get an idea about the entire content.
What Is Text Summarization?
It is a technique that shortens a long piece of content with main points outlined that gives an idea of the whole content. It becomes critical when someone needs a quick and accurate summary of very long content. Summarizing text can be expensive and time-consuming if done manually.
Machine learning and natural processing language are helpful in creating an automatic text summary. Let’s see how Natural language processing (NLP) is doing Text Summarization.
Why It Becomes A Need?
The importance of data to the current world can never be undermined. As per IDC, the total amount of data that is expected to sprout by 2025 is 180 zettabytes. There is no need to emphasize on the importance of text summarization in the age of big data. The algorithms or techniques that shorten the longer text and delivers that accurate but brief information are much needed. No need to say that, Text summarization will reduce the reading time, will be helpful in research and will help in finding more information in less time.
Types of Text Summarization
Automatic text summarization is an important aspect of natural language processing but the question is how to summarize text using NLP.
There are two prime aspects of text summarization:
- Extraction based Summarization
Extraction based automatic text summarization is an algorithm that extracts the text from the original content without making any changes in it on the basis of a defined metric. It is generally based on the weight of the essential section of text or words and their rephrasing. Different types of methods could be used to measure the weight of the sentences.
Source Text: NLP is a short form of natural language processing. It is a branch of artificial intelligence that deals with the interaction between humans and computers using natural language processing. Most NLP depends on machine learning to extract the meaning of human languages.
Summary Text: NLP deals with the interaction between computer and human and extracts the meaning of human language.
As you can see the bold words has been rephrased to extract a summary of the source text. However, it is not every time that you get a grammatically correct summary.
- Abstraction based summarization
Unlike extraction, abstraction based text summarization is more close to humans expectation. The algorithm creates sentences and phrases to express the most useful information from the original text. It avoids the grammar inconsistencies that usually happens in extraction based text summarization.
Therefore this method is more accurate and performs better than extraction but is quite difficult to develop.
Source Text: NLP is a short form of natural language processing. It is a branch of artificial intelligence that deals with the interaction between human and computers using natural language processing. Most NLP depends on machine learning to extract the meaning of human languages.
Summary: NLP, Natural language processing, extracts the meaning of human languages using machine learning.
Let’s See How Automatic Text Summarization Works
However, there are various types of extractive text summarization approaches such as Graph-theoretic, cluster-based approach, machine learning-based, etc. The most dominant techniques are TF-IDF and TextRank.
- Text summarization using term frequency (TF-IDF method)
The method is based on inverse sentence frequency and weighted term-frequency paradigm. Sentence frequency is the number of sentences containing the term. The highest-scoring sentences are picked and assembled to create a summary.
TextRank is inspired by the Google PageRank algorithm which is used for the online search result. It is based on the calculation of most ranked sentences based on the relation between the sentences.
How TextRank works:
- It splits the text into individual sentences
- Calculates the vector representation of each sentence
- The similarity matrix is then calculated and converted into a graph
- Calculates the rank of the sentences
- Top-ranked sentences create the final summary.
Perfection Is Yet To Come
Natural language processing shows a great role in machine-human interaction. It is still in progress and more researches are being carried in this sector. We could expect to see a more smart and perfect text summarization technique in future that will understand human language and work accordingly.
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