Papers with sentence simplification
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). |
| Approach: | They adapt an architecture with augmented memory capacities called Neural Semantic Encoders for sentence simplification. |
| Outcome: | The proposed architecture improves on different datasets and improves human judgments. |
Discourse-Based Sentence Splitting (2021.findings-emnlp)
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| Challenge: | Sentence splitting is a key component of sentence simplification and has been shown to help human comprehension. |
| Approach: | They propose to use a discourse connective to generate a sentence that is shorter than the input text. |
| Outcome: | The proposed models outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical. |
Learning to Paraphrase Sentences to Different Complexity Levels (2023.tacl-1)
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| Challenge: | Using unsupervised datasets, we train models on sentence complexification and same-level paraphrasing tasks. |
| Approach: | They compare two unsupervised datasets with a single supervised dataset to train models on sentence complexification and same-level paraphrasing tasks. |
| Outcome: | The proposed models outperform previous work on sentence-level targeting and improve on the ASSET simplification benchmark. |
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification (2022.findings-acl)
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| Challenge: | Existing methods for sentence simplification are supervised or unsupervised . paraphrasing captures complex edit operations, while revision-based methods provide more control and interpretability. |
| Approach: | They propose an unsupervised approach to sentence simplification that combines text generation and text revision. |
| Outcome: | The proposed method improves on the Newsela and ASSET datasets. |
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%). |
Controllable Sentence Simplification via Operation Classification (2022.findings-naacl)
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| Challenge: | Sentence simplification involves a sentence being transformed into a simpler version of itself while preserving its core meaning. |
| Approach: | They propose a controllable-simplification model that tailors simplifications to four global operations . they propose to use a dataset to train highly accurate classification systems for these operations based on syntactic or discourse structure . |
| Outcome: | The proposed model outperforms both end-to-end and controllable approaches in sentence simplification tasks. |
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases (2022.lrec-1)
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| Challenge: | MUSS trains strong models using sentence-level paraphrase data instead of labeled simplification data. |
| Approach: | They propose a multilingual unsupervised sentence simplification system that does not require labeled simplification data. |
| Outcome: | The proposed model outperforms the previous best supervised models on English, French, and Spanish benchmarks despite not using labeled simplification data. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
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. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)
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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. |
| Approach: | They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification. |
| Outcome: | The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show . |
Edit-Constrained Decoding for Sentence Simplification (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that lexically constrained decoding is effective for sentence simplification, but their constraints can be loose and may lead to sub-optimal generation. |
| Approach: | They propose an edit operation based on lexically constrained decoding for sentence simplification using a dictionary of technical terms as constraints. |
| Outcome: | The proposed method outperforms previous studies on English simplification corpora and is based on lexical paraphrasing. |
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020.acl-main)
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| Challenge: | Existing models for sentence simplification are focused on a single transformation, such as lexical paraphrasing or splitting. |
| Approach: | They propose a dataset for assessing sentence simplification in English using a crowdsourced multi-reference corpus. |
| Outcome: | The proposed dataset shows that it captures characteristics of simplicity better than other datasets. |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification (2023.findings-acl)
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| Challenge: | Existing strategies to teach pre-trained models to generate simple texts are inadequate. |
| Approach: | They propose a continued pre-training strategy to teach pre-trained models to generate simple texts by randomly masking text spans in ordinary texts. |
| Outcome: | The proposed strategy improves on lexical simplification, sentence simplification and document-level simplification tasks over existing models. |
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. |
| Approach: | They propose a document-level simplification task using Wikipedia dumps as a dataset and propose an automatic evaluation metric called D-SARI. |
| Outcome: | The proposed metric is more suitable for document-level simplification task. |
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. |
| Approach: | They propose a new learning evaluation metric that focuses on simplicity outperforming most existing metrics in terms of correlation with human judgements. |
| Outcome: | The proposed metric outperforms most existing metrics in terms of correlation with human judgements. |
Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)
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Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty
| Challenge: | integrating coreference and decomposition increases recall on rare relations by over 20%. |
| Approach: | They propose an open-source pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. |
| Outcome: | The proposed pipeline achieves a 99.8% exact-match accuracy on sentence simplification. |
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification (2023.acl-long)
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| Challenge: | Current text simplification research mostly focuses on English and on sentencelevel simplification. |
| Approach: | They propose to use a dataset of parallel, professionally written and manually aligned simplifications in plain German "plain DE" and "Einfache Sprache" they build a web harvester and experiment with automatic alignment methods to facilitate integration of non-aligned and to be-published parallel documents. |
| Outcome: | The proposed dataset of parallel, professionally written and manually aligned simplifications in plain German is extended to 750 document pairs and 3.5k sentence pairs. |
SENTA: Sentence Simplification System for Slovene (2024.lrec-main)
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| Challenge: | Sentence simplification involves converting complex sentences into more accessible forms while preserving their meaning and context. |
| Approach: | They propose a system for sentence simplification in Slovene that uses a neural classifier to identify sentences that need simplification and a large Slovenen language model to refine sentences into a simpler form. |
| Outcome: | The proposed system achieves an excellent SARI score of 41 for a large Slovene language model based on T5 architecture . it is integrated into a freely accessible, user-friendly user interface, offering a valuable service to less-fluent Slovenen users. |