@article{Bhagat_Mallick_Karia_Kaushal_2022, title={INDEPROP: Information-Preserving De-propagandization of News Articles (Student Abstract)}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21594}, DOI={10.1609/aaai.v36i11.21594}, abstractNote={We propose INDEPROP, a novel Natural Language Processing (NLP) application for combating online disinformation by mitigating propaganda from news articles. INDEPROP (Information-Preserving De-propagandization) involves fine-grained propaganda detection and its removal while maintaining document level coherence, grammatical correctness and most importantly, preserving the news articles’ information content. We curate the first large-scale dataset of its kind consisting of around 1M tokens. We also propose a set of automatic evaluation metrics for the same and observe its high correlation with human judgment. Furthermore, we show that fine-tuning the existing propaganda detection systems on our dataset considerably improves their generalization to the test set.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bhagat, Aaryan and Mallick, Faraaz and Karia, Neel and Kaushal, Ayush}, year={2022}, month={Jun.}, pages={12915-12916} }