Automatic Text Simplification of News Articles in the Context of Public Broadcasting
Diego Maupom\'e, Fanny Rancourt, Thomas Soulas, Alexandre Lachance,, Marie-Jean Meurs, Desislava Aleksandrova, Olivier Brochu Dufour, Igor Pontes,, R\'emi Cardon, Michel Simard, Sowmya Vajjala
TL;DR
This paper discusses the development of an automatic text simplification system tailored for news articles, aiming to improve accessibility and comprehension for diverse audiences.
Contribution
The authors present a novel ATS approach specifically designed for news content, addressing challenges unique to journalistic language and structure.
Findings
Improved readability scores for simplified news articles
Enhanced comprehension demonstrated in user studies
Effective handling of complex journalistic language
Abstract
This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Text Readability and Simplification · Topic Modeling