The aim of this paper is to give an overview of the state-of-art in automatic text simplification. To that end, we present the systems and prototypes according to the language the are built for, their target audience and the type of simplification (syntactic, lexical or both) they perform. Moreover, we expound the di↵erent evaluation methods that have been carried out with these systems and the...

We propose a method for acquiring plain lexical paraphrase using a Japanese dictionary in order to achieve lexical simplification for children. The proposed method extracts plain words that are the most similar to the headword from the dictionary definition. The definition statements describe the headword using plain words; therefore, paraphrasing by replacing the headword with the most similar...

Lexical Simplification consists in replacing complex words in a text with simpler alternatives. We introduce LEXenstein, the first open source framework for Lexical Simplification. It covers all major stages of the process and allows for easy benchmarking of various approaches. We test the tool’s performance and report comparisons on different datasets against the state of the art approaches. T...

We introduce a bootstrapping algorithm for regression that exploits word embedding models. We use it to infer four psycholinguistic properties of words: Familiarity, Age of Acquisition, Concreteness and Imagery and further populate the MRC Psycholinguistic Database with these properties. The approach achieves 0.88 correlation with humanproduced values and the inferred psycholinguistic features ...

This paper introduces Japanese lexical simplification. Japanese lexical simplification is the task of replacing complex words in a given sentence with simple words to produce a new sentence without changing the original meaning of the sentence. We propose a method of supervised regression learning to estimate complexity ordering of words with statistical features obtained from two types of Japa...

This paper presents three systems that took part in the lexical simplification task at SEMEVAL 2012. Speculating on what the concept of simplicity might mean for a word, the systems apply different approaches to rank the given candidate lists. One of the systems performs second-best (statistically significant) and another one performs third-best out of 9 systems and 3 baselines. Notably, the th...

In this paper, we describe the system we submitted to the SemEval-2012 Lexical Simplification Task. Our system (mmSystem) combines word frequency with decompositional semantics criteria based on syntactic structure in order to rank candidate substitutes of lexical forms of arbitrary syntactic complexity (oneword, multi-word, etc.) in descending order of (cognitive) simplicity. We believe that t...

We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data. T...

In this paper we present the results of a study directed towards developing a lexical simplification module of an automatic simplification system for Spanish, intended for readers with cognitive disabilities. We here observe the word length and frequency distribution of two sets of texts that make up our parallel corpus, and we focus on cases of information expansion (through the insertion of d...

We describe two systems for text simplification using typed dependency structures, one that performs lexical and syntactic simplification, and another that performs sentence compression optimised to satisfy global text constraints such as lexical density, the ratio of difficult words, and text length. We report a substantial evaluation that demonstrates the superiority of our systems, individua...