The Rise of Natural Language Generation

Unless you’ve been living under a rock for the past couple of months, you’ll probably have tested ChatGPT (or at the very least read about in the general press or in our blog post The future of news in media intelligence), the chatbot launched by OpenAI. Its ability to instantly create in-depth and convincing text replies to short text instructions has been one of the biggest tech stories in recent years, and rightly so: it’s a genuinely exciting development. Beyond the examples of AI-generated Seinfeld scripts or positive Fyre Festival reviews, the underlying technology has huge potential to be a crucial tool for organizations seeking to improve productivity and efficiency. 

Natural language generation (NLG) is going to be increasingly employed by organizations in 2023. It’s the “process of producing meaningful phrases and sentences in the form of natural language,” from input data. Or, in other words, it’s computers writing for us, generating text based on structured data. Practical use cases of NLG in 2023 consists of describing or summarizing input structured data at huge scale with lightning speed. This could be anything from summaries of articles or reports, to product descriptions, or meeting memos etc. Instead of teams of analysts reading, annotating, and ultimately summarizing countless articles, spreadsheets, and any other data format, NLG will do it all in a matter of seconds (or less), using exponentially larger data sets. It’s going to be an extremely impactful and time-saving tool for organizations for many years to come, enabling better decisions to be made when it matters. 

For a bit of fun, here are two paragraphs explaining NLG. Can you tell if a human wrote them? Or just one of them? Or are they both computer generated? 

1. NLG is a process of generating natural language from data. It is commonly used in machine translation, summarization, and content generation applications. NLG systems are based on artificial intelligence and computational linguistics techniques. NLG technology has seen a rapid expansion in recent years, driven by advances in machine learning and Natural Language Processing (NLP). NLG systems are now being used to generate reports, create summaries, and even generate entire articles on a variety of topics. The most well-known NLG system is Google's PageRank algorithm, which is used to generate search results. Other popular NLG applications include chatbots and digital assistants such as Siri and Alexa. NLG systems are also being used to generate news articles and create summaries of financial reports. The rise of NLG is changing the way we interact with machines and is likely to have a profound impact on the way information is consumed in the future. NLG will enable us to get information from sources that were previously inaccessible or time-consuming to process. This technology has the potential to revolutionize how we receive and use information.


2. NLG, or natural language generation, is a process of generating text from data. NLG systems have been used for years to generate reports from data sets, but the rise of deep learning has led to significant advances in the quality of NLG. The most recent NLG system, GPT-3, was released in June 2020 and is capable of generating human-like text. GPT-4 is the successor to GPT-3 and is currently in development. NLG systems are used in a variety of applications, including automatic summarization, question answering, and chatbots. NLG has also been used to generate fake news articles and reviews. As NLG systems become more advanced, they will likely play an increasingly important role in our lives.

Answer: Both were generated by an AI content generator. Pretty good, right?