Text Simplification

113 papers with code • 11 benchmarks • 20 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


Use these libraries to find Text Simplification models and implementations

Most implemented papers

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.

Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

yale-lily/medgen 28 Nov 2023

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.

DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion

google-research-datasets/discofuse NAACL 2019

We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.

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.

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.

Control Prefixes for Parameter-Efficient Text Generation

Yale-LILY/dart 15 Oct 2021

Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application.

Klexikon: A German Dataset for Joint Summarization and Simplification

dennlinger/klexikon LREC 2022

Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training.

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

microsoft/unilm 20 May 2022

We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.

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.