Challenge: Xu et al., 2016) show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification.
Approach: They evaluate neural sequence-to-sequence models for text simplification on Wikipedia and Newsela datasets.
Outcome: The proposed model can generalize across corpora and overcome challenges when tested on Wikipedia and Newsela datasets.

Similar Papers

Text Simplification from Professionally Produced Corpora (L18-1)

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Challenge: Existing approaches to Text Simplification rely on the Wikipedia-Simple Wikipedia parallel corpus, which is used for many tasks.
Approach: They propose to use the Newsela corpus to extract 550, 644 complex-simple sentence pairs from the corpus and introduce a lexical simplifier that uses the corpu to generate candidate simplifications.
Outcome: The proposed model outperforms state-of-the-art approaches and generates candidate simplifications from the newsela corpus.
CombiNMT: An Exploration into Neural Text Simplification Models (2020.lrec-1)

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Challenge: Neural Text Simplification (NMT) is a widely used technique in Machine Translation (NLP)
Approach: They present a replication study of Exploring Neural Text Simplification Models using OpenNMT and Newsela datasets.
Outcome: The proposed systems improve on the original paper by using an updated implementation of OpenNMT and the newsela corpus alongside the original Wikipedia dataset.
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.
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification (2021.acl-long)

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Challenge: Text simplification reduces the language complexity of professional content for accessibility purposes.
Approach: They propose that text simplification can be decomposed into a pipeline of tasks . they show that the pipeline can be used to predict whether a text needs to be simplified .
Outcome: The proposed model improves the performance of out-of-sample simplification tests on a blackbox lexical model . the proposed model reduces the complexity of professional text by a large margin .
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (2020.findings-emnlp)

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Challenge: Existing evaluation methods for text summarization systems are limited to in-domain setting, where supervised pre-trained models are evaluated on the same dataset.
Approach: They propose to use a cross-dataset evaluation approach to evaluate different summarization systems in a multi-domain setting.
Outcome: The proposed model can be used to evaluate text summarization systems on different datasets.
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.
Approach: They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity.
Outcome: The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1.
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)

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Challenge: Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation.
Approach: They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach.
Outcome: The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela)
Gauging the Gap Between Human and Machine Text Simplification Through Analytical Evaluation of Simplification Strategies and Errors (2023.findings-eacl)

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Challenge: Recent studies on text simplification have focused on lexical and syntactic simplification, but few studies have attempted to assess what kind of editing operations are performed by the systems in concrete terms.
Approach: They develop an analytical evaluation framework for neural text simplification systems that includes fine-grained taxonomies of simplification strategies and errors.
Outcome: The framework was used to evaluate TS models produced by human editors and multiple neural TS systems and found that human editors perform deletions and local substitutions while excessively omitting important information.
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.
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)

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Challenge: Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality.
Approach: They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context.
Outcome: The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%.

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