mlsanityData to 7 Oct 2026

Context-Preserving Text Simplification

arXiv 2105.11178 ↗First posted 24 May 2021cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 48 references that are in the map. First posted on arXiv 24 May 2021.

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References · 7

  1. 2018A Survey on Open Information Extractioncited by 51
  2. 2019A Unified Linear-Time Framework for Sentence-Level Discourse Parsingcited by 15
  3. 2019Transforming Complex Sentences into a Semantic Hierarchycited by 15
  4. 2016Getting More Out Of Syntax with PropScited by 8
  5. 2019DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and Germancited by 4
  6. 2019Controllable Sentence Simplification: Employing Syntactic and Lexical Constraintscited by 4
  7. 2018Graphene: A Context-Preserving Open Information Extraction Systemcited by 3

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