Have you ever had to summarize a legal document full of technical jargon and legal proceedings? If so, you can surely remember the tedious job of extracting relevant data from a document, be it technical or literary.
For average humans it might take days, if not weeks, to meticulously sort through a 50-page technical document, weed out the irrelevant information, and produce a comprehensive summary of that document, all the while not jeopardizing its accuracy.
Even after putting your best effort into text summarization, you can’t discount the chance of data redundancy or worse, omitting something vital. But what if you can get a nonhuman alternative to do it for you? This alternative will never get tired of reading, sorting, and analyzing, its brain will not get overworked, and it will be as near perfect as you can get.
Sounds interesting, right? That’s where the concept of automatic text summarization comes into the equation.
What Is Automatic Text Summarization?
For those who are interested in machine learning, automatic text summarization might be a familiar term. Even if you are not an artificial intelligence (AI) nerd, your friends Alexa or Siri can surely put this in context for you!
AI has many different sectors, and natural language processing (NLP) is one of the oldest and most researched ones. Automatic text summarization combines NLP and machine learning to produce a comprehensive summary of the text.
Automatic text summarization can produce two types of summary, one extractive and the other abstractive. An extractive summary works the same way you highlight a physical document: It singles out the key phrases and numbers and produces a fairly accurate technical summary.
An abstractive summary is a little complex. It doesn’t just put together the keywords from the text. It does exactly what a human summarizer would do: It creates a summary from the main idea of the text using its own words and putting them in a meaningful sequence.
Let’s dive deeper into it and find out how enterprises are taking advantage of this innovation.
Suppose you need to find out the current trend of an industry from various new articles. But you have barely enough time to even glimpse through the headlines, let alone read all of those to reach the crux of their ideas. In that case, a multiple text summarization can even help you extract the summary of multiple news articles.
And here’s how it works. News articles generally follow an inverted pyramid scheme for information flow. The first few sentences of an article usually contain the key information about the news, and the rest of the article provides a lot of background information.
While this scheme was developed to help editors easily cut down the article when necessary while keeping the key information intact for printing, it has also aided the progress of automatic text summarization. To summarize a news article, it only has to go through the first few lines, and it will get the crux of the news content.
SEO and Search Marketing
Search engine optimization and marketing is an ever-competitive world. And content is the key marketing strategy here. In order to stay ahead of your competitors, you have to monitor and track what others are putting up as content for the consumers. And every day the digital market is being flooded with tons of content; manually sorting through all these contents is very time-consuming.
Automatic text summarization can help you in this regard. It can help you focus on new ideas and save you from manually sorting through the torrent of content that keeps repeating the same idea.
Financial and investment decisions require meticulous research and sorting through a large amount of content. It is both true for individual investors or investment corporations. Automatic text summarization designed for analyzing and condensing financial documents can help you out in this regard.
For instance, people who are not familiar with extracting data from financial graphs or data can use the help of automatic text summarization to create a well-rounded summary of those financial visualizations.
Chatbots and Autoresponder
Nearly every website has some built-in chatbots that pop up on the website’s homepage. You can even create a basic level auto responder in Facebook messenger. And they can carry on a fairly informative conversation with the website user.
The idea behind these chatbots is to provide users uninterrupted services without human intervention. These chatbots rely on natural language processing (NLP) and machine learning for auto text summarization.
Social Media Marketing
Businesses develop various resource materials such as e-books, newsletters, blogs, or white papers to reach out to their audience. However, these lengthy documents will get more chances to reach their target audience if they are broken down into short summaries by automatic text summarization.
That way, a user can readily understand if the content will be worth reading. Besides, these short excerpts are more suitable for circulating through various social media platforms.
There are some rumors in the grapevine about AI replacing human writers. When prominent media platforms like the New York Times or the Washington Post are already using AI to produce some content, it might not be a completely baseless rumor.
A writer needs to read first in order to generate content. Automatic text summarization has mastered the art of reading and extracting data more efficiently than a human. And that makes it a viable candidate to replace human writers in the content industry.
As automatic text summarization can already summarize or condense a text to its basic elements, it can also create new content. And that’s what creating content mostly involves: analyzing existing content, extracting their essence, and creating something new. So don’t be alarmed if this is an AI produced content!
Remote Working and Video Conferencing
Text summarization doesn’t only work to create text to text summary; it can also be used for speech-to-text summarization. And in this post COVID remote working culture, that’s a huge relief for employees around the globe.
For instance, if you need meeting notes from a virtual conference, you can get a speech-to-text summarization application to do that for you. it will be more efficient than having to listen to that audio record over and over to develop a summary manually.
The Bottom Line
Our digital world is flooded with content. At present, there might be 100 trillion words floating on the Internet. However, not all of them carry the same importance. Extracting what’s relevant and what’s not is a whole new ballgame.
NLP and automatic text summarization have become a lifesaver when it comes to summarizing long and tedious documents, be it technical, financial, legal, medical, or even literary. From academia to businesses, every sector can reap its own benefits. And we are still just scratching the surface of its true potential.