Context-Preserving Text Simplification
This addresses the need for more coherent text simplification in NLP, though it appears incremental by building on existing sentence splitting methods.
The paper tackles the problem of text simplification by preserving discourse-level context, achieving 89% precision in capturing contextual hierarchy and 69% average precision for rhetorical relation classification.
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, input sentences are converted into a hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. Hence, as opposed to previously proposed sentence splitting approaches, which commonly do not take into account discourse-level aspects, our TS approach preserves the semantic relationship of the decomposed constituents in the output. A comparative analysis with the annotations contained in the RST-DT shows that we are able to capture the contextual hierarchy between the split sentences with a precision of 89% and reach an average precision of 69% for the classification of the rhetorical relations that hold between them.