Papers Text Simplification

The Common Readability Formula & Five Adjusted Readability Formulas for Text Simplification, Medical Documents and Other General Uses

Traditional readability formulas, or equations, are inaccurate and measure highly limited linguistic properties. Despite the recent machine learning-based readability assessment models, many researchers insist on using t…

Automatic Lexical Simplification for Turkish

In this paper, we present the first automatic lexical simplification system for the Turkish language. Recent text simplification efforts rely on manually crafted simplified corpora and comprehensive NLP tools that can an…

Towards more patient friendly clinical notes through language models and ontologies

Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their he…

The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification

Abstract In order to simplify sentences, several rewriting operations can be performed, such as replacing complex words per simpler synonyms, deleting unnecessary information, and splitting long sentences. Despite this m…

Few-shot Controllable Style Transfer for Low-Resource Multilinugal Settings

Style transfer is the task of rewriting an input sentence into a target style while approximately preserving its content. While most prior literature assumes access to large style-labelled corpora, recent work (Riley et …

Divide-and-Conquer Text Simplification by Scalable Data Enhancement

Text simplification, whose aim is to reduce reading difficulty, can be decomposed into four discrete rewriting operations: substitution, deletion, reordering, and splitting. However, due to a large distribution discrepan…

Combining Paraphrase Pre-trained Model and Controllable Rules for Unsupervised Sentence Simplification

Although neural sequence-to-sequence models for sentence simplification achieve some progress, they still suffer from the data sparsity problem and are lack of controllability. This paper proposes a two-stage approach fo…

A New Dataset and Efficient Baselines for Document-level Text Simplification in German

The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity. We introduce a newly collected data set of German texts, collected from the Swiss new…

Control Prefixes for Parameter-Efficient Text Generation

Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application. However, it uses the same dataset-level tuned prompt for all examples in the dataset. We exte…

Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings

Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al. 2021) …

Document-Level Text Simplification: Dataset, Criteria and Baseline

Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simpl…

Text Simplification for Comprehension-based Question-Answering

Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and understand while preserving the content and approximating the original meaning. Text simpl…

How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks

The general goal of text simplification (TS) is to reduce text complexity for human consumption. This paper investigates another potential use of neural TS: assisting machines performing natural language processing (NLP)…

A Syntax-Aware Edit-based System for Text Simplification

Edit-based text simplification systems have attained much attention in recent years due to their ability to produce simplification solutions that are interpretable, as well as requiring less training examples compared to…

Exploring German Multi-Level Text Simplification

We report on experiments in automatic text simplification (ATS) for German with multiple simplification levels along the Common European Framework of Reference for Languages (CEFR), simplifying standard German into level…

JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models

Predicting the complexity level of a word or a phrase is considered a challenging task. It is even recognized as a crucial step in numerous NLP applications, such as text rearrangements and text simplification. Early res…

Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer

Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no study has been found using this pre-trained mo…

Automatic Text Simplification for Social Good: Progress and Challenges

A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification

Cartography of Natural Language Processing for Social Good (NLP4SG): Searching for Definitions, Statistics and White Spots

The range of works that can be considered as developing NLP for social good (NLP4SG) is enormous. While many of them target the identification of hate speech or fake news, there are others that address, e.g., text simpli…