Text Simplification

63 papers with code • 5 benchmarks • 11 datasets

Text Simplification is the task of reducing the complexity of the vocabulary and sentence structure of text while retaining its original meaning, with the goal of improving readability and understanding. Simplification has a variety of important societal applications, for example increasing accessibility for those with cognitive disabilities such as aphasia, dyslexia, and autism, or for non-native speakers and children with reading difficulties.

Source: Multilingual Unsupervised Sentence Simplification

Libraries

Use these libraries to find Text Simplification models and implementations

Most implemented papers

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

rekriz11/sockeye-recipes NAACL 2019

Sentence simplification is the task of rewriting texts so they are easier to understand.

Controllable Sentence Simplification

facebookresearch/access LREC 2020

Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical.

Felix: Flexible Text Editing Through Tagging and Insertion

google-research/google-research Findings of the Association for Computational Linguistics 2020

We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.

HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture

farooqzaman1/HTSS Information Processing and Management 2020

Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score.

FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model

denizyuret/fastsubs-googlecode 24 May 2012

Lexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction.

Unsupervised Sentence Simplification Using Deep Semantics

shashiongithub/Sentence-Simplification-ACL14 WS 2016

We present a novel approach to sentence simplification which departs from previous work in two main ways.

Optimizing Statistical Machine Translation for Text Simplification

cocoxu/simplification TACL 2016

Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus.

Readability-based Sentence Ranking for Evaluating Text Simplification

nishkalavallabhi/complexity-features 18 Mar 2016

We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking.

Sentence Simplification with Deep Reinforcement Learning

XingxingZhang/dress EMNLP 2017

Sentence simplification aims to make sentences easier to read and understand.

Exploring Neural Text Simplification Models

senisioi/NeuralTextSimplification ACL 2017

Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction.