# "TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

arXiv: https://arxiv.org/abs/2104.00782 · HTML: https://mlsanity.com/paper/2104.00782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 67 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 12 of 12)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?](https://mlsanity.com/paper/2001.00463/) (2019)
- [The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources](https://mlsanity.com/paper/1705.06947/) (2017)
- ["I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter](https://mlsanity.com/paper/2101.07183/) (2021)
- [The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans](https://mlsanity.com/paper/1804.03461/) (2018)
- [TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic](https://mlsanity.com/paper/2012.02586/) (2020)
- [Exploring the Ideological Nature of Journalists' Social Networks on Twitter and Associations with News Story Content](https://mlsanity.com/paper/1708.06727/) (2017)
- [Meet Malexa, Alexa's Malicious Twin: Malware-Induced Misperception Through Intelligent Voice Assistants](https://mlsanity.com/paper/2002.03466/) (2020)
- [TrollHunter2020: Real-Time Detection of Trolling Narratives on Twitter During the 2020 US Elections](https://mlsanity.com/paper/2012.02606/) (2020)

## Abstract (arXiv)

This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal- or conservative-leaning agenda. The Out-of-Context Summarizer also suggests hashtag keywords to bolster the polarization of the summary, in case one is inclined to take it to Twitter, Parler or other platforms for trolling. Out-of-Context Summarizer achieved 79% precision and 99% recall when summarizing COVID-19 articles, 93% precision and 93% recall when summarizing politically-centered articles, and 87% precision and 88% recall when taking liberally-biased articles out of context. Summarizing valid sources instead of synthesizing fake text, the Out-of-Context Summarizer could fairly pass the "adversarial disclosure" test, but we didn't take this easy route in our paper. Instead, we used the Out-of-Context Summarizer to push the debate of potential misuse of automated text generation beyond the boilerplate text of responsible disclosure of adversarial language models.