Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges

TL;DR

This paper provides a large-scale, systematic review of text generation research from 2017 to 2024, organizing work into five core tasks and examining evaluation methods and cross-cutting challenges. It introduces a reproducible methodology for literature collection (search, automated filtering, manual assessment) and catalogs 244 papers, emphasizing dataset availability, metrics, and safety concerns. The analysis highlights persistent issues such as bias, hallucinations, and privacy, and outlines promising directions like improved factuality evaluation, safer prompting, and more efficient computing. Overall, the work serves as a comprehensive roadmap for researchers to navigate tasks, evaluation, and responsible deployment in text generation.

Abstract

Text generation has become more accessible than ever, and the increasing interest in these systems, especially those using large language models, has spurred an increasing number of related publications. We provide a systematic literature review comprising 244 selected papers between 2017 and 2024. This review categorizes works in text generation into five main tasks: open-ended text generation, summarization, translation, paraphrasing, and question answering. For each task, we review their relevant characteristics, sub-tasks, and specific challenges (e.g., missing datasets for multi-document summarization, coherence in story generation, and complex reasoning for question answering). Additionally, we assess current approaches for evaluating text generation systems and ascertain problems with current metrics. Our investigation shows nine prominent challenges common to all tasks and sub-tasks in recent text generation publications: bias, reasoning, hallucinations, misuse, privacy, interpretability, transparency, datasets, and computing. We provide a detailed analysis of these challenges, their potential solutions, and which gaps still require further engagement from the community. This systematic literature review targets two main audiences: early career researchers in natural language processing looking for an overview of the field and promising research directions, as well as experienced researchers seeking a detailed view of tasks, evaluation methodologies, open challenges, and recent mitigation strategies.

Figures (2)

• Figure 1: Taxonomy of the main tasks in text generation and their respective challenges.
• Figure 2: Pipeline of this systematic review. Red-colored boxes show papers we excluded; Green-colored boxes show papers we added.