Being able to understand information is a key factor for a self-determined life and society. It is also very important for participating in democratic processes. The study of automatic text simplification is often limited by the availability of high quality material for the training and evaluation on automatic simplifiers. This is true for English, but more so for less resourced languages like Spanish, Catalan and Italian. In order to fill this gap, we present a corpus of original texts for these 3 languages, with high quality simplification produced by human experts in text simplification. It was developed within the iDEM project to assess the impact of Easy-to-Read (E2R) language for democratic participation. The original texts were compiled from domains related to this topic. The corpus includes different text types, selected based on relevance, copyright availability, and ethical standards. All texts were simplified to E2R level. The corpus is particularity valuable because it includes the first annotated corpus of its kind for the Catalan language. It also represents a noteworthy contribution for Spanish and Italian, offering high-quality, human-annotated language resources that are rarely available in these domains. The corpus will be made freely accessible to the public.
Figures & tables
Language
Original
Simplified
Catalan
L’Ajuntament de Barcelona posa a disposició de la ciutadania mitjans que permeten rectificar les seves dades personals (per exemple, un nom mal escrit) o actualitzar-les (per exemple, l’adreça postal), sens perjudici del dret de rectificació reconegut a la normativa de protecció de dades.
L’Ajuntament de Barcelona dona la possibilitat a la ciutadania de canviar o corregir les seves dades personals. Per exemple, si el nom està mal escrit, es pot arreglar. També es pot actualitzar informació com per exemple l’adreça postal. Tot això sense perdre el dret per corregir les dades.
Italian
La Convenzione ONU sui Diritti dell’Infanzia e Adolescenza riconosce a tutti i bambini e ragazzi i diritti fondamentali, come il diritto alla protezione, all’educazione e a una vita priva di discriminazioni.
C’è la Convenzione ONU sui Diritti dell’Infanzia e Adolescenza. Una convenzione è come un accordo. ONU sta per Organizzazione delle Nazioni Unite. È un’unione di quasi tutti i paesi del mondo. Nella convenzione, ci sono i diritti di tutti i bambini e tutti i ragazzi. Per esempio, hanno questi diritti: * essere al sicuro * studiare * vivere senza essere trattati male
Spanish
El panel europeo de ciudadanos sobre la lucha contra el odio en la sociedad ha presentado sus recomendaciones a la Comisión Europea. Durante 3 fines de semana, 150 ciudadanos seleccionados aleatoriamente, en representación de los 27 Estados miembros de la UE, se han reunido para debatir las distintas causas y factores que incitan al odio en la UE y los diferentes ámbitos de actuación para combatirlos.
El panel dedicado a lucha contra el odio, ya hizó sus recomendaciones a la Comisión Europea. 150 ciudadanos de la Unión Europea, elegidos al azar, estuvieron reunidos durante 3 fines de semana para debatir sobre el odio en la Unión Europea y como solucionarlo.
Table 1: Corpus Examples
Language
Topic
Catalan
Sustainability & Participation; Social Policy & Disability Accessibility; Data Protection; Social Services & Taxation; Digital Governance; Public Policy Discussion; Social Justice; Health & Governance; Justice & Governance; Civic Participation
Italian
Children’s Rights & Advocacy; Civic Engagement & Social Responsibility; EU Economic Policy; EU Elections; Organizational Law & Statute; Civic Participation; Legislative Reform & Policy; Referendum & Electoral Systems; Social Policy : Economic & Political Analysis; Social Policy & Advocacy; Government/NGO Policy Communication; Legislative Reform ("Taglia leggi" law); EU Institutions; Environmental Policy & Local Governance
Spanish
Social Policy; Health & Governance; EU Economic Policy; Justice & Governance; Political Activism; Justice & Legal Affairs; Communism & Ideology; EU Legislation; Civic Participation; EU Elections; EU Institutions
Table 2: Corpus’ Topics
Category
Words
Percentage
Informative texts
4892
41.94%
Political & Ideological articles
1146
9.82%
News articles
3817
32.72%
Policy & Legislative Documents
1810
15.52%
Total
11665
100.00%
Table 3: Percentage per category: Spanish
Category
Words
Percentage
Informative texts
6090
52.64%
Social Justice & Public Policy Analysis
1652
14.28%
Policy & Administrative Documents
3828
33.09%
Total
11570
100.00%
Table 4: Percentage per category: Catalan
Category
Words
Percentage
Informative Texts
4425
42.56%
Institutional & Legal Documents
1247
11.99%
Political & Legal Analysis
2680
28.24%
Advocacy & Social Responsibility
1789
17.21%
Total
10398
100.00%
Table 5: Percentage per category: Italian
Language
Sentences
Words
Words per
Segments
Sentence
Segment
Orig
Simp
Orig
Simp
Orig
Simp
Spanish
354
1290
11665
10883
32.95
8.44
Catalan
380
405
11570
14279
41.32
35.26
Italian
325
718
10398
12230
31.99
17.03
Table 6: Distribution of number of sentence segments and words, and the word per sentence segment ratio for each language
Simplification Criteria
Count
Percentage
Use of paragraph breaks or line breaks to separate ideas into different sentences
241
22.8%
Use of frequent/common verbs
116
11.0%
Use of short, simple, and commonly used nouns
111
10.5%
Reordering of sentences into logical structure: subject + verb + predicate
84
7.9%
Elimination or substitution of abstract terms, foreign words, vague content, and/or technical jargon
79
7.5%
Use of indicative mood and simple tenses (present, simple future, simple past, and imperfect)
48
4.5%
Table 7: Most frequently used annotation tags: Spanish
Simplification Criteria
Count
Percentage
Choose words that are easily understandable and familiar to the target group.
261
55.30%
Only use common and frequent adverbs. Avoid adverbs ending in -ment.
48
10.17%
When listing elements related to a single idea in one sentence, use commas to separate them.
34
7.20%
Write quantities and figures using numbers instead of words.
22
4.66%
Create a glossary or include glosses in the document if there are terms that are difficult to understand.
22
4.66%
The most common punctuation marks may be used: period (.), comma (,), question mark (?),
19
4.03%
Table 8: Most frequently used annotation tags: Catalan
Simplification Criteria
Count
Percentage
Use of commonly used verbs
292
13.34%
Use of periods or commas to separate sentences with multiple ideas
271
12.38%
Elimination or modification of abstract terms, foreignisms, vague content, and/or technical jargon
264
12.06%
Use of short, simple, and commonly used nouns
255
11.65%
Use of commonly used adjectives
163
7.45%
Use of commonly used adverbs
143
6.53%
Table 9: Most frequently used annotation tags: Italian
Alignment
Count
Percentage
1:1
587
55.43
1:2
139
13.13
1:3
116
10.95
1:4
88
8.31
1:5
49
4.63
1: > 5
80
7.55
Table 10: Alignments over the whole corpus
Simplification Criteria
Count
Percentage
Use of paragraph breaks or line breaks to separate ideas into different sentences
241
22.8%
Use of frequent/common verbs
116
11.0%
Use of short, simple, and commonly used nouns
111
10.5%
Reordering of sentences into logical structure: subject + verb + predicate
84
7.9%
Elimination or substitution of abstract terms, foreign words, vague content, and/or technical jargon
79
7.5%
Use of indicative mood and simple tenses (present, simple future, simple past, and imperfect)
48
4.5%
Table 11: All annotation tags with frequencies: Spanish
Simplification Criteria
Count
Percentage
Choose words that are easily understandable and familiar to the target group.
261
55.30%
Only use common and frequent adverbs. Avoid adverbs ending in -ment.
48
10.17%
When listing elements related to a single idea in one sentence, use commas to separate them.
34
7.20%
Write quantities and figures using numbers instead of words.
22
4.66%
Create a glossary or include glosses in the document if there are terms that are difficult to understand.
22
4.66%
The most common punctuation marks may be used: period (.), comma (,), question mark (?),
19
4.03%
Table 12: All annotation tags with frequencies: Catalan
Simplification Criteria
Count
Percentage
Use of commonly used verbs
292
13.34%
Use of periods or commas to separate sentences with multiple ideas
271
12.38%
Elimination or modification of abstract terms, foreignisms, vague content, and/or technical jargon
264
12.06%
Use of short, simple, and commonly used nouns
255
11.65%
Use of commonly used adjectives
163
7.45%
Use of commonly used adverbs
143
6.53%
Table 13: All annotation tags with frequencies: Italian
Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including language learners and readers with limited literacy. Despite its importance, large-scale, high-quality datasets for training and evaluating text simplification models remain scarce for languages other than English. This paper reports an experimental study on the collection and processing of crowd-sourced simplification data from comparable corpora to construct a corpus suitable for both training and testing text simplification systems across multiple languages (Catalan, English, French, Italian and Spanish). We report mechanisms for sentence-level alignment from document-level data. The resulting dataset of the aligned sentence pairs is publicly available.
Kenji Hilasaca, Nouran Khallaf, Serge Sharoff
Centre for Translation, Localisation and Interpreting Studies School of Languages, Cultures and Societies University of Leeds, UK
Text simplification aims to make complex texts easier to understand while preserving their original meaning. Recent large language models can perform simplification through prompting, but it remains unclear whether adding explicit linguistic information about sentence complexity to the prompt improves their outputs. We investigate this question for multilingual sentence-level Easy-to-Read simplification in Catalan, Spanish, and Italian. Using Qwen3-8B, we compare a baseline prompt, a gold-cue prompt enriched with gold linguistic cues, and a predicted-cue prompt enriched with automatically predicted cues. We evaluate the outputs using SARI, BLEU, chrF, and BERTScore, and complement this evaluation with a manual qualitative analysis. Predicted-cue prompting obtains the best overall scores across all four metrics, although the gains over the baseline are small. Gold-cue prompting does not consistently improve over the baseline, and results vary across languages. These findings indicate that cue-based prompting can influence multilingual Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.
Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constructing personalized parallel corpora is costly, while existing large language model (LLM)-based readability control methods rely on pre-labeled sentence corpora and primarily target English. We propose Re-RIGHT, a unified reinforcement learning framework for adaptive multilingual text simplification without parallel corpus supervision. We first show that prompting-based lexical simplification at target proficiency levels (CEFR, JLPT, TOPIK, and HSK) performs poorly at easier levels and for non-English languages, even with state-of-the-art LLMs such as GPT-5.2 and Gemini 2.5. To address this, we collect 43K vocabulary-level data across four languages (English, Japanese, Korean, and Chinese) and train a compact 4B policy model using Re-RIGHT, which integrates three reward modules: vocabulary coverage, semantic preservation, and coherence. Compared to the stronger LLM baselines, Re-RIGHT achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency.