Document-Level Planning for Text Simplification
Liam Cripwell, Joël Legrand, Claire Gardent · 2023
Most existing work on text simplification is limited to sentence-level inputs, with attempts to iteratively apply these approaches to document-level simplification failing to coherently preserve the discourse structure of the document.We hypothesise that by providing a high-level view of the target document, a simplification plan might help to guide generation.Building upon previous work on controlled, sentence-level simplification, we view a plan as a sequence of labels, each describing one of four sentence-level simplification operations (copy, rephrase, split, or delete).We propose a planning model that labels each sentence in the input document while considering both its context (a window of surrounding sentences) and its internal structure (a token-level representation).Experiments on two simplification benchmarks (Newsela-auto and Wikiauto) show that our model outperforms strong baselines both on the planning task and when used to guide document-level simplification models.