Automatic Text Simplification of News Articles in the Context of Public Broadcasting
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Abstract (arXiv)
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).
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 26 Dec 2022.
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References · 16
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
- 2020MLSUM: The Multilingual Summarization Corpuscited by 121
- 2017Sentence Simplification with Deep Reinforcement Learningcited by 109
- 2020ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformationscited by 101
- 2019Controllable Sentence Simplificationcited by 78
- 2019EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editingcited by 63
- 2019EASSE: Easier Automatic Sentence Simplification Evaluationcited by 50
- 2018Semantic Structural Evaluation for Text Simplificationcited by 30
- 2018Unsupervised Neural Text Simplificationcited by 29
- 2022Lexical Simplification Benchmarks for English, Portuguese, and Spanishcited by 18
4 more
- 2021Rethinking Automatic Evaluation in Sentence Simplificationcited by 17
- 2022Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitutioncited by 12
- 2020Simple-QE: Better Automatic Quality Estimation for Text Simplificationcited by 5
- 2017The Helsinki Neural Machine Translation Systemcited by 2
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