NLG Systems Wiki

Dive into the Systems section of NLG‑Wiki.org, where you will find an exhaustive catalogue of natural language generation (NLG) systems — from prototype research engines to industrial platforms. This wiki serves as a one-stop resource for exploring the technology, methodology and implementations behind NLG.

What You’ll Find

• Open-source frameworks for data-to-text, summarisation and dialogue generation

• Commercial engines used in industry for automated reporting, content generation, and analytics

• Research implementations highlighting experimental architectures and evaluation studies

• Classic systems tracing the evolution of NLG from template-based to neural generation

How the Directory Works

Each system entry typically includes:

• Name of system and contributing organization

• Architectural type (e.g., rule-based, statistical, neural)

• Domain of application (e.g., weather reports, healthcare, chatbots)

• Availability and licence (open source, academic prototype, commercial)

• Reference papers, links to code repositories and demos
  
  

Why Use This Wiki?

• Compare systems based on architecture, domain and maturity

• Identify toolkits you can download or build upon in your project

• Access bibliographic references for state-of-the-art NLG methods

• Track NLG’s development through historical and current system listings

Who Benefits

• Researchers and students drilling into NLG architectures and evaluation

• Engineers and developers selecting engines for practical applications

• Practitioners in industry, needing to survey available NLG solutions

• Educators and learners, building context around NLG system taxonomy

Getting Started

Use the navigation filters to check systems by:

• Input/output types (e.g., structured data → text, image → caption)

• System licence and access level

• Research maturity or production-readiness
  Once you select a system, you can explore its details, access its interface or review linked publications and code.

Explore the NLG Systems Wiki today — uncover the tools powering automatic text generation and deepen your understanding of NLG platforms.