Text summarizers are able to take the most important information from the source documents and produce a reader-friendly summary.
The amount of data available online is limitless. Think about a normal college student, who has to go through thousands of pages of documents each semester. That is why text summarization is necessary.
The main purpose of text summarization is to get the most precise and useful information from a large document and eliminate the irrelevant or less important ones.
Text summarization can be done either manually, which is time-consuming, or through machine algorithms and AIs, which takes very little time and is a better option.
What is Text Summarization?
Text summarization is the process of turning larger documents into shorter and precise paragraphs or sentences. The process brings out information that is crucial, and also ensures that the meaning of the paragraph stays the same. This helps reduce the time to understand large papers like research articles, without skipping any vital information.
What is Automatic Text Summarization
Human beings are generally good at perceiving what is important and what is not. This makes them efficient at summarizing large texts. Machines, on the other hand, do not have the perception of what is important or not.
They need to insert proper coding and programs into machines so that they too can create summarized texts just like humans. And the process of text summarization done with machines or AI programs is known as automatic text summarization.
However, there are challenges to automatic text summarization. The first problem is selecting the appropriate information from the main document. After that, the summarizer has to express the final summary in a reader-friendly manner.
By overcoming these challenges, automatic text summarizers aim to optimize topic coverage and readability.
Text Summarization based on Input Type
Based on the type of input, there are two types of text summarization-
1. Single Document
This is basically self-explanatory. Single document summarizers aim to summarize one single document.
2. Multiple Document
Multiple Document or multiple text summarization includes multiple documents and the final paper has to contain summarized information from all the documents.
Types of Text Summarization based on Outcome
There are two types of text summarization based on the outcome. These are-
1. Extraction based Text Summarization
Extraction-based summarization is a simple process. The important words and phrases are taken out of the original text and compiled together to make the summary.
There is no rephrasing or using synonyms in this summarization process. The words are taken out as they are and slightly rearranged to give the sentence a structure. Because there is no use of synonyms and no rephrasing, it makes the summarization process easier.
For example, if the original text is “Luna and Neville washed their hands before they greeted each other.” then an extraction-based summary of the text would be “Luna and Neville greeted each other”. Most machines and AI programs use this type of summarization.
2. Abstraction based Text Summarization
Abstraction-based summarization is more complex than extraction-based summarization. It takes out the original and important sentence from a text document and rephrases it with proper synonyms. That way, it will look like a completely different text but have the same meaning as the original text.
That is why it is difficult because figuring out the right synonyms and rephrasing by keeping the meaning the same is tough. For example, if the original text is “Peter was climbing down the stairs hurriedly. He slipped and fell down and broke his ankle”, then the abstraction summary would be “Peter broke his ankle after he slipped from running down the stairs.”
Summarization based on Context
Based on the type of information used, text summarization can be categorized into three types-
Domain knowledge is used in domain-specific summarization. Domain-specific summarizers can be integrated with specific context, knowledge, and words. For example, models can be integrated with words used in medical science so that it can better understand scientific articles on medical science and summarize them.
2. Query Based
Query-based summaries mostly contain information about natural language questions. This is similar to Google’s search results.
Sometimes we type in questions on the search bar and Google shows us websites or articles that have answers to our questions. It shows us a snippet or summary of an article related to the question we searched.
Generic summarizations are not programmed to make any assumptions like the domain-specific or query-based summarizers. It just condenses or summarizes the information from the source document.
The Benefits of Using Text Summarization
There are quite a few significant benefits of text summarization such as-
- They make reading easier
- It saves time
- It helps memorize information easily
- It boosts the work rate efficiency
What is a Good Summary?
The primary goals of text summarization are-
- Optimal topic coverage
- Optimal readability
To ensure these two factors, there are a few evaluation criteria. One of them is salience or keeping the most important aspect. A summarizer has to be programmed to catch the most important information of the source document.
The final summary has to be of perfect length. It should not be too long or too short. The structure needs to be reader-friendly. The sentences have to be coherent and make sense.
It should not have weird pronouns in its formation. The entire summary has to be balanced. This means it has to cover all the important aspects of the document and of course, the entire article has to be grammatically correct.
Lastly, the sentences should not feel redundant. If a summarizer can maintain all these criteria, it will be able to produce reader-friendly summaries that can help us in many different ways.
Text summarization has become a significant part of the life of researchers, students, and people who go through huge textual documents every day. With the development of technology and AIs, it is possible that one day, automatic text summarization will be just as good and clear as manual text summarization.