Data-to-text is the NLP task of generating natural language descriptions from structured data — automatically converting tables, databases, knowledge bases, and other structured information into fluent, accurate text, enabling automated report writing, data narration, and content generation from any structured data source.

What Is Data-to-Text Generation?

• Definition: Converting structured data into natural language text.
• Input: Structured data (tables, JSON, databases, APIs, knowledge bases).
• Output: Fluent, accurate natural language description.
• Goal: Make data accessible and understandable through text.

Why Data-to-Text?

• Accessibility: Not everyone reads charts and tables — text is universal.
• Automation: Generate narratives from data without human writers.
• Scale: Produce thousands of data reports simultaneously.
• Personalization: Tailor data narratives to different audiences.
• Consistency: Standardized, accurate descriptions every time.
• Real-Time: Generate descriptions as data updates.

Data-to-Text Architecture

Traditional Pipeline: 1. Content Selection: Choose which data to mention. 2. Document Planning: Organize selected content into discourse structure. 3. Sentence Planning: Determine sentence structure and aggregation. 4. Surface Realization: Generate actual words and grammatical text.

Neural End-to-End:

• Single model maps structured data → text directly.
• Models: Transformer encoder-decoder (BART, T5, GPT).
• Benefit: Simpler pipeline, more natural output.
• Challenge: Hallucination — may generate text not supported by data.

Hybrid Approaches:

• Content selection via rules/templates + neural surface realization.
• Combine reliability of rules with fluency of neural generation.
• Fact verification modules to catch hallucinations.

Input Data Types

• Tables: Relational data in rows and columns.
• Key-Value Pairs: Attribute-value structures.
• RDF Triples: Subject-predicate-object knowledge representations.
• Time Series: Temporal numeric data.
• JSON/XML: Hierarchical structured data.
• SQL Results: Database query outputs.
• APIs: Live data feeds and web services.

Applications

Journalism:

• Automated news from sports statistics, financial data, election results.
• Example: "The Lakers defeated the Celtics 112-104, led by James' 32 points."

Business Intelligence:

• Automated report narratives from dashboards and KPIs.
• Example: "Q3 revenue grew 15% to $2.3M, exceeding forecast by $200K."

Healthcare:

• Patient record summarization, lab result descriptions.
• Example: "Blood glucose levels have trended downward from 180 to 120 over 30 days."

Weather:

• Automated weather reports from meteorological data.
• Example: "Expect partly cloudy skies with temperatures reaching 72°F."

E-Commerce:

• Product descriptions from spec sheets.
• Review summaries from rating data.

Challenges

• Hallucination: Generating facts not in the data — critical issue.
• Faithfulness: Ensuring text accurately reflects data.
• Content Selection: Deciding what's important to mention.
• Numerical Reasoning: Correctly computing and expressing quantities.
• Aggregation: Summarizing across multiple data points.
• Domain Adaptation: Different domains need different styles and vocabulary.

Evaluation Metrics

• BLEU/ROUGE: N-gram overlap with reference text (limited).
• PARENT: Precision/recall against table content (better for faithfulness).
• Faithfulness Metrics: Check if generated text is entailed by data.
• Human Evaluation: Fluency, accuracy, relevance, informativeness.

Key Datasets & Benchmarks

• WebNLG: RDF triples → text.
• ToTTo: Table → one-sentence description.
• WikiTableText: Wikipedia tables → text.
• RotoWire: NBA box scores → game summaries.
• E2E NLG: Restaurant data → descriptions.
• DART: Multiple data-to-text datasets unified.

Tools & Frameworks

• Models: T5, BART, GPT-4, Llama for generation.
• Frameworks: Hugging Face Transformers, OpenNMT.
• NLG Platforms: Arria, Automated Insights, Narrative Science.
• Evaluation: GEM benchmark suite for comprehensive evaluation.

Data-to-text is the bridge between structured data and human understanding — it transforms raw numbers and records into narratives that anyone can comprehend, enabling automated, scalable, and accessible data communication across every domain.