On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)
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| Challenge: | Text simplification is a hot issue in the field of natural language generation (NLG). |
| Approach: | They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences. |
| Outcome: | The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%). |
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| Challenge: | Recent work on document simplification has focused on sentence-level inputs but fails to preserve the discourse structure. |
| Approach: | They explore various systems that use document context within the simplification process . they investigate the performance and efficiency tradeoffs of system variants . |
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Document-Level Text Simplification: Dataset, Criteria and Baseline (2021.emnlp-main)
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| Challenge: | Text simplification is a valuable technique, but research on it is limited. |
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A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification (L18-1)
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Diverse Pretrained Context Encodings Improve Document Translation (2021.acl-long)
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Sentence Simplification with Memory-Augmented Neural Networks (N18-2)
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| Challenge: | Sentence simplification aims to simplify the content and structure of complex sentences . prior work has focused on monolingual machine translation (MT) and tree-based MT (TBMT). |
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| Challenge: | Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. |
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What Context Features Can Transformer Language Models Use? (2021.acl-long)
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| Challenge: | Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. |
| Approach: | They propose to use lexical and structural information to ablate usable information in transformer language models. |
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Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
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When and Why is Document-level Context Useful in Neural Machine Translation? (D19-65)
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| Challenge: | Recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. |
| Approach: | They extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document- level context in NMT. |
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Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
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| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
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