A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification

Abstract

We present a detailed evaluation and analysis of neural sequence-to-sequence models for text simplification on two distinct datasets: Simple Wikipedia and Newsela. We employ both human and automatic evaluation to investigate the capacity of neural models to generalize across corpora, and we highlight challenges that these models face when tested on a different genre. Furthermore, we establish a strong baseline on the Newsela dataset and show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification.

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