The Definition of Natural Language Generation (NLG)

This article is published by AllBusiness.com, a partner of TIME.

What is "Natural Language Generation"?

Natural Language Generation (NLG) refers to the use of artificial intelligence to produce human-like text based on structured or unstructured data inputs.

NLG is a subset of natural language processing (NLP) and focuses on transforming data into coherent, meaningful language that can be easily understood by humans.

NLG is commonly used to create narratives from numerical data, automate report generation, and even generate personalized content. NLG aims to bridge the gap between machines and humans by enabling AI systems to communicate in ways that mimic human language and thought.

Examples of Natural Language Generation:

Automated News Writing: NLG is used by news agencies to automatically generate news reports from raw data. For example, financial updates or sports summaries can be created using structured data like stock prices or game scores.
Weather Reports: Some meteorological services use NLG to turn weather data into plain language weather reports. Based on temperature, humidity, and wind data, these systems can produce daily forecasts in natural language.
Customer Support Chatbots: NLG powers many chatbots that provide customer support by turning customer queries into tailored responses. This can help businesses handle frequently asked questions or guide users through troubleshooting steps.
Business Reports: Companies use NLG to generate business insights and reports from financial data, sales statistics, or operational metrics. By automating the generation of performance summaries, NLG reduces the workload for analysts.
Personalized Marketing Content: E-commerce companies use NLG to automatically create product descriptions and marketing messages tailored to individual customers based on their browsing history or purchasing patterns.

Benefits of Natural Language Generation:

Scalability: NLG allows for the mass generation of content with minimal human intervention, making it easier to scale tasks like report writing, content creation, or customer interaction.
Efficiency: NLG saves time by automating repetitive writing tasks. Instead of manually generating business reports or summaries, companies can rely on AI to quickly convert data into readable content.
Personalization: NLG enables personalized communication, whether in marketing, customer service, or product recommendations. It tailors responses or content to individual needs based on available data.
Consistency: NLG ensures that the tone, style, and quality of content remain consistent across various reports or communications, reducing the chances of human error or inconsistencies in messaging.
Cost-Effective: Automating content generation through NLG can reduce costs, as businesses can rely less on human writers or analysts for routine tasks, such as drafting reports or generating routine summaries.

Limitations of Natural Language Generation:

Contextual Limitations: While NLG can generate text based on available data, it often lacks a deep understanding of context, leading to overly simplistic or incorrect responses. For example, a weather report might miss nuances that a human would naturally include.
Creativity Restrictions: NLG is highly effective for data-driven tasks but struggles with creative writing or generating content that requires nuanced understanding. For instance, writing a novel or a complex essay is beyond its current capabilities.
Data Dependency: NLG relies on structured or semi-structured data to function effectively. If the data is incomplete or inaccurate, the resulting text could be misleading or incorrect.
Limited Human-Like Interaction: Although NLG can produce coherent text, the interaction often feels robotic. It lacks the depth of understanding, emotional intelligence, and cultural awareness that human writers have.
Ethical Concerns: Automating text generation can raise ethical issues, especially in cases where NLG is used to generate fake news, biased content, or misleading information. This could potentially manipulate public opinion or deceive consumers.

Summary of Natural Language Generation:

NLG is playing a significant role in industries that need to automate repetitive writing tasks or generate large-scale content from data.

With its ability to streamline processes and increase efficiency, NLG has become a valuable tool in fields like journalism, customer service, and business analytics.