Automatic Text Simplification (ATS) has emerged as a key area of research within the field of Natural Language Processing, aiming to improve access to information by reducing the linguistic complexity of texts. Simplification can be applied at various levels—lexical, syntactic, semantic, and stylistic—and must be tailored to meet the needs of different target audiences, such as individuals with cognitive disabilities, low-literacy readers, or non-native speakers. This work introduces a tool that automatically adapts Spanish texts into Easy-to-Read format, enhancing comprehension for people with cognitive or reading difficulties. The proposal is grounded in a critical review of existing Spanish-language resources and addresses the need for accessible, well-documented solutions aligned with official guidelines, reinforcing the potential of text simplification as a strategy for inclusion.

The T2Know project explores the application of natural language processing technologies to build a semantic platform for scientific documents using knowledge graphs. These graphs will interconnect meaningful sections from different documents, enabling both trend analysis and the generation of informed recommendations. The project’s objectives include the development of entity recognition systems, the definition of user and document profiles, and the linking of documents through transformer-based technologies. Consequently, the extracted relevant content will go beyond standard metadata such as titles and author affiliations, extending also to other key sections of scientific articles, including references, which are treated as integral components of the knowledge representation.

In the age of knowledge, the democratisation of information facilitated through the Internet may not be as pervasive if written language poses challenges to particular sectors of the population. The objective of this paper is to present an overview of research-based automatic text simplification tools. Consequently, we describe aspects such as the language, language phenomena, language levels simplified, approaches, specific target populations these tools are created for (e.g. individuals with cognitive impairment, attention deficit, elderly people, children, language learners), and accessibility and availability considerations. The review of existing studies covering automatic text simplification tools is undergone by searching two databases: Web of Science and Scopus. The eligibility criteria involve text simplification tools with a scientific background in order to ascertain how they operate. This methodology yielded 27 text simplification tools that are further analysed. Some of the main conclusions reached with this review are the lack of resources accessible to the public, the need for customisation to foster the individual’s independence by allowing the user to select what s/he finds challenging to understand while not limiting the user’s capabilities and the need for more simplification tools in languages other than English, to mention a few.

This paper presents the ongoing work conducted within the ClearText project, specifically focusing on the resource creation for the simplification of Spanish for people with cognitive disabilities. These resources include the CLEARSIM corpus and the Simple.Text tool. On the one hand, a description of the corpus compilation process with the help of APSA is detailed along with information regarding whether these texts are bronze, silver or gold standard simplification versions from the original text. The goal to reach is 18,000 texts in total by the end of the project. On the other hand, we aim to explore Large Language Models (LLMs) in a sequence-to-sequence setup for text simplification at the document level. Therefore, the tool’s objectives, technical aspects, and the preliminary results derived from early experimentation are also presented. The initial results are subject to improvement, given that experimentation is in a very preliminary stage. Despite showcasing flaws inherent to generative models (e.g. hallucinations, repetitive text), we examine the resolutions (or lack thereof) of complex linguistic phenomena that can be learned from the corpus. These issues will be addressed throughout the remainder of this project. The expected positive results from this project that will impact society are three-fold in nature: scientific-technical, social, and economic.

Fever Shared 2.0 Task is a challenge meant for developing automated fact checking systems. Our approach for the Fever 2.0 is based on a previous proposal developed by Team Athene UKP TU Darmstadt. Our proposal modifies the sentence retrieval phase, using statement extraction and representation in the form of triplets (subject, object, action). Triplets are extracted from the claim and compare to triplets extracted from Wikipedia articles using semantic similarity. Our results are satisfactory but there is room for improvement.

The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a)positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.