Papers Text Simplification

ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, a…

Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification

Parallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be expl…

Automated Text Simplification as a Preprocessing Step for Machine Translation into an Under-resourced Language

In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language. We use th…

The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries

In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a…

Cross-Sentence Transformations in Text Simplification

Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations span beyond single sentences (e.g. joining and re- ordering sentences). In this paper…

Metaphors in Text Simplification: To change or not to change, that is the question

We present an analysis of metaphors in news text simplification. Using features that capture general and metaphor specific characteristics, we test whether we can automatically identify which metaphors will be changed or…

Simplification-induced transformations: typology and some characteristics

The purpose of automatic text simplification is to transform technical or difficult to understand texts into a more friendly version. The semantics must be preserved during this transformation. Automatic text simplificat…

Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table

Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural…

Complex Word Identification as a Sequence Labelling Task

Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve …

Negative Lexically Constrained Decoding for Paraphrase Generation

Paraphrase generation can be regarded as monolingual translation. Unlike bilingual machine translation, paraphrase generation rewrites only a limited portion of an input sentence. Hence, previous methods based on machine…

Controllable Text Simplification with Lexical Constraint Loss

We propose a method to control the level of a sentence in a text simplification task. Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alter…

EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing

We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are varian…

Transforming Complex Sentences into a Semantic Hierarchy

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate…

Neural Text Simplification in Low-Resource Conditions Using Weak Supervision

Neural text simplification has gained increasing attention in the NLP community thanks to recent advancements in deep sequence-to-sequence learning. Most recent efforts with such a data-demanding paradigm have dealt with…

Subjective Assessment of Text Complexity: A Dataset for German Language

This paper presents TextComplexityDE, a dataset consisting of 1000 sentences in German language taken from 23 Wikipedia articles in 3 different article-genres to be used for developing text-complexity predictor models an…

Generating Animations from Screenplays

Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into ani…

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via …

Neural Versus Non-Neural Text Simplification: A Case Study

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

Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propo…

Reference-less Quality Estimation of Text Simplification Systems

The evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics requir…