mlsanityData to 7 Oct 2026

Context-Aware Document Simplification

arXiv 2305.06274 ↗First posted 10 May 2023cs.CLcited by 3

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Abstract (arXiv)

To date, most work on text simplification has focused on sentence-level inputs. Early attempts at document simplification merely applied these approaches iteratively over the sentences of a document. However, this fails to coherently preserve the discourse structure, leading to suboptimal output quality. Recently, strategies from controllable simplification have been leveraged to achieve state-of-the-art results on document simplification by first generating a document-level plan (a sequence of sentence-level simplification operations) and using this plan to guide sentence-level simplification downstream. However, this is still limited in that the simplification model has no direct access to the local inter-sentence document context, likely having a negative impact on surface realisation. We explore various systems that use document context within the simplification process itself, either by iterating over larger text units or by extending the system architecture to attend over a high-level representation of document context. In doing so, we achieve state-of-the-art performance on the document simplification task, even when not relying on plan-guidance. Further, we investigate the performance and efficiency tradeoffs of system variants and make suggestions of when each should be preferred.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 10 May 2023.

Builds on

27 of 50 references.

References · 27

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2020Longformer: The Long-Document Transformercited by 3,362
  4. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  5. 2017Sentence Simplification with Deep Reinforcement Learningcited by 107
  6. 2020Neural CRF Model for Sentence Alignment in Text Simplificationcited by 106
  7. 2019EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editingcited by 63
  8. 2020Controllable Text Simplification with Explicit Paraphrasingcited by 53
  9. 2019EASSE: Easier Automatic Sentence Simplification Evaluationcited by 50
  10. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  11. 2020Controlling Style in Generated Dialoguecited by 38
  12. 2020A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systemscited by 34
15 more
  1. 2020Iterative Edit-Based Unsupervised Sentence Simplificationcited by 33
  2. 2020Elaborative Simplification: Content Addition and Explanation Generation in Text Simplificationcited by 27
  3. 2021Text Simplification by Taggingcited by 27
  4. 2021Document-Level Text Simplification: Dataset, Criteria and Baselinecited by 27
  5. 2022On the Ethical Considerations of Text Simplificationcited by 20
  6. 2019Discourse Level Factors for Sentence Deletion in Text Simplificationcited by 19
  7. 2021Rethinking Automatic Evaluation in Sentence Simplificationcited by 17
  8. 2022SMART: Sentences as Basic Units for Text Evaluationcited by 17
  9. 2020Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplificationcited by 12
  10. 2021Knowledge-based Review Generation by Coherence Enhanced Text Planningcited by 10
  11. 2021Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reorderingcited by 10
  12. 2022Controlled Text Reductioncited by 10
  13. 2017A Sentence Simplification System for Improving Relation Extractioncited by 7
  14. 2022GRS: Combining Generation and Revision in Unsupervised Sentence Simplificationcited by 6
  15. 2021Zero-Shot Controlled Generation with Encoder-Decoder Transformerscited by 5

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.