Automatic Text Simplification of News Articles in the Context of Public Broadcasting
Diego Maupomé, Fanny Rancourt, Thomas Soulas, Alexandre Lachance, Marie-Jean Meurs, Desislava Aleksandrova, Olivier Brochu Dufour, Igor Pontes, Rémi Cardon, Michel Simard, Sowmya Vajjala
arXiv:2212.13317v1h-index: 20
Originality Synthesis-oriented
AI Analysis
This addresses text simplification for public broadcasting audiences, but it appears incremental as it builds on existing ATS methods.
The authors tackled automatic text simplification for news articles in public broadcasting, but the abstract does not provide results or concrete numbers.
This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Université de Montréal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).