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Data-Driven Sentence Simplification: Survey and Benchmark

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.cl-1.4.pdf

Summary

Automatic sentence simplification (SS) rewrites a sentence so it is easier to read while keeping its core meaning and grammaticality. It can help readers with aphasia, dyslexia, or autism, non-native speakers, and downstream NLP tasks such as parsing or MT. The authors review English SS systems that learn rewriting operations from parallel corpora of original and simplified sentences, now the dominant approach. They explain how each family of models is trained and describe the resources used for training and testing, mainly Wikipedia- and Newsela-based corpora. Because published results relied on different data and metric implementations, the authors also build a benchmark. It uses publicly released system outputs on shared test sets (PWKP, TurkCorpus) and scores them with the same metrics: BLEU, SARI, and Flesch-Kincaid. A new per-transformation analysis shows which rewriting operations each system actually performs. The review finds that no approach yet handles all operations well, splitting in particular. Discourse and document-level effects remain largely unexplored. The survey serves as an entry point to the field and sets out a research agenda.

Key points

  • Systems are grouped into four families: SMT-based (e.g., Moses PBSMT, SBSMT), grammar-based (QG + ILP, STSGs), semantics-assisted (Boxer deep semantics), and neural sequence-to-sequence (RNN or Transformer, with RL, MTL, or PPDB). Sequence labeling is discussed as an additional approach.
  • Systems are compared on four core rewriting operations: substitution, deletion, reordering, and splitting. SMT models manage the first three, but produce good splits only when syntax or semantics is modeled explicitly.
  • Grammar-based models represent splits more naturally, but choosing and ordering rules is difficult. Sequence labeling lacks a manually annotated corpus for training and analysis.
  • Main datasets covered: EW-SEW, WikiSmall/PWKP, WikiLarge, Newsela, and TurkCorpus. Early human-simplification studies analysed 104 English news articles and 200 Spanish ones.
  • The authors argue that the field should shift from sentence-level to document-level simplification.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation · Natural language processing

Topics: Machine translation

Cited by

100 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 96 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00370

@article{alvamanchego2020data,
  title = {{Data-Driven Sentence Simplification: Survey and Benchmark}},
  author = {Alva-Manchego, Fernando and Scarton, Carolina and Specia, Lucia},
  journal = {Computational Linguistics},
  year = {2020},
  doi = {10.1162/coli\_a\_00370},
  url = {https://doi.org/10.1162/coli_a_00370}
}

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