# Persuasive Natural Language Generation -- A Literature Review

arXiv: https://arxiv.org/abs/2101.05786 · HTML: https://mlsanity.com/paper/2101.05786/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 90 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [DebateSum: A large-scale argument mining and summarization dataset](https://mlsanity.com/paper/2011.07251/) (2020)
- [Exploring the Role of Argument Structure in Online Debate Persuasion](https://mlsanity.com/paper/2010.03538/) (2020)
- [Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!](https://mlsanity.com/paper/1807.08998/) (2018)
- [An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text](https://mlsanity.com/paper/1912.06745/) (2019)
- [Echoes of Persuasion: The Effect of Euphony in Persuasive Communication](https://mlsanity.com/paper/1508.05817/) (2015)
- [Experiments in Detecting Persuasion Techniques in the News](https://mlsanity.com/paper/1911.06815/) (2019)

## Built on by (most cited first, 10 of 10)

- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [The Persuasive Power of Large Language Models](https://mlsanity.com/paper/2312.15523/) (2023)
- [Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans](https://mlsanity.com/paper/2209.13020/) (2022)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Audience-Centric Natural Language Generation via Style Infusion](https://mlsanity.com/paper/2301.10283/) (2023)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)
- [A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling](https://mlsanity.com/paper/2510.15081/) (2025)
- [Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences](https://mlsanity.com/paper/2601.04925/) (2026)

## Abstract (arXiv)

This literature review focuses on the use of Natural Language Generation (NLG) to automatically detect and generate persuasive texts. Extending previous research on automatic identification of persuasion in text, we concentrate on generative aspects through conceptualizing determinants of persuasion in five business-focused categories: benevolence, linguistic appropriacy, logical argumentation, trustworthiness, tools and datasets. These allow NLG to increase an existing message's persuasiveness. Previous research illustrates key aspects in each of the above mentioned five categories. A research agenda to further study persuasive NLG is developed. The review includes analysis of seventy-seven articles, outlining the existing body of knowledge and showing the steady progress in this research field.