Natural Language Generation (NLG)
Natural language generation (NLG) is a part of artificial intelligence (AI) and computational linguistics. NLG deals with the automatic production of natural language texts by a machine from computer-accessible data. It is described as the conversion of data into text. NLG is the counterpart to Natural Language Understanding (NLU).
The core tasks in the NLG process include:
- Content determination: deciding what information to include in the generated text.
- Text planning: organising the information into a coherent structure.
- Sentence planning: Determining how the information is distributed across individual sentences and paragraphs, including the addition of cohesive elements.
- Realisation: The actual generation of the text based on the syntax, morphology and orthography of the target language.
NLG is used in various areas:
- E-commerce: creation of product descriptions.
- Robot journalism: generation of news texts from databases.
- Content marketing: creation of advertising texts.
- Chatbots: Automation of communication in dialogue systems.
- Automated reporting
Technologies and methods in NLG include:
- Data-to-text: Conversion of structured data into natural language text.
- Rule-based systems: Use of explicit rules and templates to generate text.
- Statistical models: Use of statistical information from large amounts of text to generate text.
- Neural networks: Use of deep learning models for text generation.
- Large Language Models (LLMs): Use of pre-trained language models such as GPT for text generation.
- Hybrid approaches: Combination of different methods.
NLG systems can range from simple fill-in-the-blank templates to complex systems with sophisticated linguistic models. The choice of suitable technology depends on the requirements of the application in question.