Nlg: natural language generation – turning data into narratives
NLG: Natural Language Generation is a subfield of artificial intelligence and Natural Language Processing (NLP) that focuses on automatically producing human-understandable text from structured or unstructured data. Essentially, NLG transforms data (like numbers in a spreadsheet, analysis results, or key points from a report) into written or spoken narratives. It’s a key technology behind automated reporting, personalized product descriptions, and certain forms of AI-generated content (AI and content creation).
The challenge: from structure to fluency
Transforming raw or structured data into text that sounds natural, coherent, and grammatically correct is NLG’s primary challenge. NLG systems use various AI algorithms and techniques (from template-based approaches to Deep Learning neural networks) to achieve this. The process typically involves several stages:
- Content Planning: Determining which information from the source data is most important to convey.
- Microplanning/Sentence Planning: Structuring the information into logical sentences and choosing appropriate words.
- Realization: Generating the final text, adhering to grammatical and stylistic rules.
Achieving output that doesn’t sound robotic or repetitive requires sophisticated AI Models.
Ensuring accuracy and data fidelity
The text generated by NLG must accurately reflect the information in the source data (AI Training Data). There’s a risk that the NLG system might misinterpret data, draw incorrect conclusions, or omit crucial information. Ensuring the factual accuracy of NLG output is vital, especially for applications like financial reporting or medical summaries. Validation and verification mechanisms are necessary as part of structuring AI governance.
Customization and tone/style control
Beyond simply conveying information, NLG can be used to tailor the tone and style of the generated text for specific audiences or purposes. For example, a financial report will require a formal tone, while a personalized product description might be more conversational. The challenge lies in building NLG systems that allow this level of control over style (adapting AI tone to brand voice) while maintaining consistency and accuracy. Prompt engineering can play a role here.
Applications of nlg in marketing and communication
NLG has numerous potential applications in AI for Marketing and communication (AI in communication strategy):
- Auto-generating campaign performance reports.
- Creating personalized product descriptions for e-commerce.
- Drafting marketing emails or social media posts based on data points.
- Generating summaries of long documents or articles.
- Powering chatbots with more natural-sounding responses.
Brandeploy: structuring content for or from nlg
Brandeploy interacts with NLG in two main ways. Firstly, structured, on-brand content managed within Brandeploy (e.g., key product features in a template) could potentially serve as *input* for an NLG system to generate longer descriptions. Secondly, and more significantly, if an NLG system generates marketing copy drafts, Brandeploy provides the content automation platform to embed this text within visually compliant templates (brand governance platform) and to manage the review and approval process (creative workflow automation). This ensures NLG-generated text is presented professionally and in alignment with the brand.
Turn your data into compelling stories with NLG. Understand how the technology works and where it can add value. Discover how Brandeploy helps integrate NLG-generated content into your compliant brand materials. Schedule a demo.
