Review Maps 257 Text‑Generation Papers Across Five Tasks

On August 6 2026, Jonas Becker, Jan Philip Wahle, Bela Gipp and Terry Ruas published a comprehensive review titled *Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges* in the Journal of Artificial Intelligence Research (JAIR). The paper surveys recent progress in large‑language models and other generative systems that have become increasingly accessible over the past decade.¹

The authors identified 257 papers spanning from January 2017 to December 2025, far exceeding earlier efforts such as Zhao et al.’s 30‑paper survey from 2018. By extending the time window back two years and expanding the dataset by an order of magnitude, the study offers a more complete picture of how research has evolved during the rise of transformer‑based architectures.¹

Central to the analysis is a taxonomy of five main tasks: open-ended generation, summarization, translation, paraphrasing, and question answering. For each task the review lists key subtasks—such as multi-document summarization for summarization or complex reasoning for QA—and evaluates characteristics most relevant to practitioners looking to benchmark new models against established baselines.¹

The article also assesses current evaluation approaches. It distinguishes between model-free metrics, model-based methods, and human evaluation protocols, noting that many popular automatic scores still rely heavily on subjective judgments when applied to creative language outputs.¹

Task-specific challenges emerge prominently across the literature. The authors point out gaps in datasets for multi-document summarization, a lack of coherence in story generation systems, and persistent difficulties with complex reasoning required for question answering—all issues highlighted repeatedly since GPT-3’s release in May 2020 by OpenAI.¹

Beyond individual tasks, the review aggregates nine common challenges shared by all subfields: bias, reasoning, hallucinations, misuse, privacy, interpretability, transparency, datasets, and computing cost. These themes echo concerns raised in recent industry reports about factual accuracy and ethical deployment of large language models.¹

Expert commentary underscores how this work builds directly upon Zhao et al.’s earlier call for systematic analysis while expanding both scope and depth. By covering 257 papers versus roughly thirty in prior surveys, it offers researchers an unprecedented reference point for future studies. Moreover, the discussion around hallucinations is especially timely given ongoing debates over factual fidelity in models such as those developed at OpenAI after GPT‑3; addressing these problems remains crucial even as model sizes grow and training techniques improve.¹

In sum, Becker, Wahle, Gipp and Ruas deliver a thorough data-driven map of text generation literature that will serve as a foundational resource for early-career researchers and seasoned experts alike, offering clarity amid the evolving AI landscape.