Comprehensive Review Maps the Landscape of Text Generation Research and Open Challenges

A major review of recent text generation research has catalogued the field's rapid expansion, examining over 250 academic papers published between early 2017 and the end of 2025. The work synthesizes how the discipline has evolved alongside the rise of large language models and identifies the core tasks, assessment approaches, and persistent difficulties that researchers must address.

Core Tasks in Text Generation#

The review organizes text generation work into five primary categories. Open-ended text generation covers creative and unrestricted language production. Summarization condenses longer documents into concise versions. Translation converts text between languages. Paraphrasing reformulates content while preserving meaning. Question answering generates responses to posed queries. Each task carries its own set of characteristics and specialized subtasks that require distinct methodological approaches.

How Systems Are Evaluated#

Researchers assess text generation quality using three broad evaluation strategies. Model-free methods rely on statistical measures that don't require training—tools like BLEU and ROUGE scores. Model-based approaches leverage trained systems to judge output quality. Human evaluation brings subjective judgment from readers and raters. The review notes that no single evaluation method captures all dimensions of quality, and the choice of metric often depends on the specific task being measured.

Specific and Shared Challenges#

Task-specific obstacles emerge in individual domains. Multi-document summarization lacks sufficient datasets for development. Story generation struggles with coherence across longer narratives. Question answering systems falter when reasoning becomes complex.

Beyond these domain-specific issues, nine broader challenges cut across all text generation work:

• Bias in model outputs and training data
• Reasoning capabilities and logical consistency
• Hallucinations—generating plausible but false information
• Misuse and potential harmful applications
• concerns in data and outputs