# Controlled Neural Sentence-Level Reframing of News Articles

arXiv: https://arxiv.org/abs/2109.04957 · HTML: https://mlsanity.com/paper/2109.04957/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity](https://mlsanity.com/paper/2010.10652/) (2020)
- [Plug and Play Autoencoders for Conditional Text Generation](https://mlsanity.com/paper/2010.02983/) (2020)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [Issue Framing in Online Discussion Fora](https://mlsanity.com/paper/1904.03969/) (2019)
- [Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces](https://mlsanity.com/paper/2010.02976/) (2020)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames](https://mlsanity.com/paper/2104.11030/) (2021)

## Built on by (most cited first, 4 of 4)

- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media](https://mlsanity.com/paper/2211.03634/) (2022)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)

## Abstract (arXiv)

Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.