Content, shared in the form of blogs, articles and other written forms, is the one of the fastest and easiest ways to share information online, whether individually or by a business. No wonder that each day, there are a staggering 2 million blogs being created to add to the 600 million blogs spanning 1.7 billion websites just in the United States alone! In today’s world of big-data, this form of content in fact constitutes nearly one-third of all big data in the world.
So, given the ubiquitous presence of this text data, wouldn’t companies, organizations, and individuals stand a huge benefit, as they have this plethora of resources and rich information across these various sources to rely upon? Well, it’s not that simple. Big data’s sheer volume - the differentiating factor when compared to past amounts of limited data - is also ironically what lends to its drawbacks.
According to one estimate, about 43% of companies are not equipped with the right tools to filter out unnecessary information, which costs them millions, as they are left with heaps of irrelevant data to manually sift through. And this is exactly the problem that arises when there are blog articles that are thousands of words each, most of which aren’t critical for their consumption and commercial progress.
Additionally, in today’s day and age, social media platforms are growing to be a very efficient way for any business to publicize, advertise, or announce their work to the masses. Given this, many inevitably desire to share article content via social platforms. But social platforms have limitations in how much information can be shared. Twitter restricts tweets to 280 characters, and posts on platforms such as LinkedIn limit content to 700 characters. So what happens now? Well, this is where summarization of blogs comes into play.
Humans can manually peruse each article, analyze the key themes, extract particular sentences that convey those themes, and condense the blog into a chosen length with only the critical sentences. Or, they can read through the entire article, analyze the key messages, and create a summary in their own words. This may seem manageable if an individual was only required to effectively condense one such lengthy article. But what if there comes a need to perform condensation in bulk, where one-thousand-word blog articles becomes 500 two-thousand-word blogs? Well, this would now dramatically increase reading and researching time, leading to a wastage of valuable time, effort, and brainpower.
Well, fortunately there is a much more powerful, faster, and more efficient solution. Enter Artificial Intelligence.
Thanks to the rising power of Artificial Intelligence, specifically Machine Learning and Natural Language Processing, robust algorithms can perform text summarization within moments and can extract key messages from any given blog to produce a condensed version that accurately conveys crucial information.
And in our modern society, with the rise of big data and AI, there are companies that specialize in creating effective tools that can utilize NLP techniques like these to perform text summarization. One such tool is Pictory.
The image above presents the before-and-after of Pictory’s summarization abilities. To the left, we see a lengthy (~1,500 words) blog article with highlighted portions, which represent the select sentences that Pictory’s ML algorithm has flagged as key. To the right, we see the summarized version of the blog (~250 words) with only the crucial information.
We’ll stop here for now, but in Part 2 of this series, we will break this process down to find out how tools such as Pictory are able to harness AI and ML to summarize text within moments.