1. What is Natural Language Generation (NLG)?

Natural Language Generation, or NLG, is a subfield of artificial intelligence. It can extract and process large amounts of data and then share that information using human sounding language. In other words, the technology tells a story in the same way a person would. Pretty amazing, right?

Imagine your boss asks you to take an Excel document with thousands of data points, extract the most relevant information, and put it into a report. You would spend hours combing through the data and trying to find the highlights. Natural Language Generation can do it in seconds, composing sentences and entire paragraphs with the most relevant insights.

But it does not just generate reports. It is also useful in many real world scenarios. And this brings us to our next question.

2. Where can we find NLG in real life?

You might think that this type of technology is not present in our day to day life. Nothing could be further from the truth. When you do these things you are in contact with NLG:

• Online shopping. Product descriptions can be written automatically using NLG.
• Talking to Alexa. When we interact with our voice assistant, NLG technology is used to respond to our requests.
• Playing video games. Call of Duty generates weekly game summaries thanks to natural language generation.

In highly regulated industries such as life sciences, NLG is also used to transform complex scientific data into structured clinical documentation. Pharmaceutical and biotechnology companies often need to convert large clinical datasets into clear narratives and regulatory reports. NLG can automatically generate medical content from these datasets, helping teams produce clinical documentation, clinical trial summaries, and regulatory reports more efficiently.

So it is likely that you may have already interacted with Natural Language Generation without realizing.

3. What can NLG do for companies?

The amount of data that can be generated in one minute is mind blowing. Globally, more than 150,000,000 million emails and 12 million text messages are sent every 60 seconds. Businesses have to make decisions based on data, and managing it can be very difficult.

Companies need to analyze and interpret this huge amount of data in the most efficient and cost effective way possible. Artificial intelligence through Natural Language Generation can provide a solution by extracting ideas and communicating them in natural language.

In industries like pharmaceutical research and clinical development, the challenge is even greater. Clinical trials can involve thousands of patients and millions of data points. NLG technology can scan these complex datasets and automatically generate structured medical content such as patient safety narratives, summaries of clinical results, or sections of clinical study reports.

This makes NLG particularly valuable for organizations looking to automate clinical trial data summarization and medical writing workflows.

In this way employees can spend their time on tasks with greater added value and leave repetitive and tedious work to technology. Ultimately this solution relieves the workload and increases productivity without involving employees in tasks that can be easily automated.

4. What is the future of NLG?

Every year there is more progress in the world of natural language generation. In 2020 OpenAI surprised us with its GPT-3 model, a technology capable of programming and talking.

Today advances in AI are also transforming specialized sectors such as life sciences research and development. AI platforms powered by NLG are helping medical writing teams automate the creation of clinical documentation, streamline regulatory workflows, and summarize large volumes of clinical data.

As regulatory requirements become more complex, many organizations are exploring AI for clinical documentation and regulatory writing automation to improve efficiency and consistency.

So it seems that the world of natural language generation is advancing quickly and is increasingly present in our daily lives. It is a more efficient way of communicating content to people.

5. Is NLP the same thing as NLG?

In Narrativa we have already talked about Natural Language Processing or NLP. But NLG and NLP are not the same. While NLP analyzes a conversation and finds out what ideas are being communicated, NLG extracts the most important ideas from the data and shares them using natural language.

NLP takes care of the reading while NLG takes care of the writing.

6. What is the difference between NLG and generative AI?

NLG (Natural Language Generation) is a type of AI that turns data into text, such as reports or weather summaries. It usually follows rules or templates.

Generative AI is a broader type of AI that creates new content such as text, images, or music. It is more flexible and creative.

• 📄 NLG makes text from data.
• 🎨 Generative AI creates many different types of content.
• 🤖 Agentic AI takes actions to achieve goals.

NLG is just one part of generative AI, focused specifically on text generation.

Natural language generation in life sciences

7. Can NLP and NLG be used by the life sciences industry to help in regulatory documentation and submission?

Yes. Narrativa’s NLP and NLG technologies have been used by leaders in the field of clinical research to automate time consuming processes needed to create clinical study reports (CSRs), including patient safety narratives and Tables, Lists, and Figures (TLFs).

When tested by medical writing teams, the outcomes produced by our solutions were reported to be comparable to the work of a proficient human medical writer in terms of accuracy. They also highlighted how Narrativa® Navigator saved significant amounts of time, ranging from weeks to several months.

Instead of manually reviewing thousands of pages of clinical data, AI systems can automatically summarize datasets, identify relevant safety information, and generate structured documentation aligned with regulatory standards.

As the life sciences industry continues to generate larger and more complex datasets, technologies such as NLP and NLG are becoming essential tools for clinical research organizations, pharmaceutical companies, and medical writing teams that need to transform data into regulatory compliant documentation quickly and accurately.