Definition
Natural Language
Generation (NLG), a subfield of natural language processing, is an AI powered software
process that produces natural written or spoken language from structured and
unstructured data. It enables computers to respond to users in human language
that they can comprehend, rather than in a way a computer might. For instance,
after processing customer input (such as commands to voice assistants, queries
to chatbots, calls to help centers or feedback on survey forms), NLG can be
employed to give a personalized response that can easily be understood, making
human-like responses from voice assistants and chatbots possible.
Let’s use a
flight status generation as an example;
With this data input; Flight: Air Peace P4 7134, Route: Lagos-Abuja, Status: Delayed, New time: 3:45 PM, Gate: 12
NLG will produce this output: "Air Peace flight
P4 7134 from Lagos to Abuja is delayed until 3:45 PM. Please proceed to Gate 12
for boarding updates."
In addition, NLG can also be used for turning numerical data input and other complex data into clear reports that we can easily comprehend. For instance, NLG might be used to generate financial reports or weather updates automatically [1].
A natural language generation process [2].
Origin
The concept of
NLG originated in the 1960s when researchers began exploring methods to
programmatically generate language-based content. Initial versions of NLG
systems relied on rule-based approaches, laying the foundation for future
advancements.
The development of NLG saw a drastic change in direction towards data-driven and machine learning-based models, making possible more nuanced and contextually relevant content generation. This development has been driven by the increasing availability of big data and the advancement of AI technologies [3].
Context and Usage
Natural Language
Generation application has increased in fields where large volumes of
structured data need to be communicated in a human-readable form:
- E-Commerce and Product Descriptions: By producing descriptions for thousands of products using specifications from databases, NLG systems help save manual writing effort and ensures consistency.
- Automated Reporting: This can be seen in journalism, finance and weather forecasting.
- Business Intelligence Dashboards: Using natural language generation, Data dashboards can give insights, improving accessibility for non-technical stakeholders.
- Customer Service & Chatbots: Virtual assistants and bots use NLG to craft responses based on user intent and backend system data.
- Medical Reporting: NLG is uses structured Electronic Health Records (EHRs), to generate patient summaries and diagnosis, reducing documentation workload for clinicians [4].
Why it Matters
NLG plays a key role in daily aspects of life, like news, forecasts, updates and voice search features in search engines. What's more, NLG can be key in business scalability as it produces amounts of quality content that would be hard to achieve manually. Although it can automate content development, saving time and money, natural language generation still needs human oversight.
In Practice
Yseop is a good example of natural language generation in practice. Yseop is a Natural Language Generation Company that offers an NLG platform for businesses. The company’s platform is built to help users to generate human-like narratives from data. Yseop’s NLG technology is used by businesses across different industries such as finance, healthcare, and sports. The platform is based on advanced machine learning algorithms that analyze data and generate narratives in real time [5].
See Also
Related NLP and
Text Processing terms:
- Natural Language Processing (NLP): Field of AI focused on enabling computers to understand and work with human language
- Natural Language Understanding (NLU): AI capability to comprehend and interpret human language meaning and intent
- Speech Analytics: Extracting insights and patterns from audio speech data
- Speech Recognition: Converting spoken language into written text
- Syntax Analysis: Understanding sentence structure
References
- Qualtrics. (2025). What is Natural Language Generation (NLG)?
- Aiola. (2025). Natural Language Generation (NLG)
- Lark Editorial Team. (2023). Natural Language Generation.
- Geeksforgeeks. (2025). Artificial Intelligence | Natural Language Generation
- Yseop. (2023). The Top 10 Natural Language Generation Companies in 2023