French Biomedical Text Simplification: When Small and Precise Helps

Cardon, Rémi; Grabar, Natalia

doi:10.18653/v1/2020.coling-main.62

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…We have seen above that our system currently tends to poorly handle technical terms, often missing from WOLF. We plan to investigate solutions to this limitation, for example by applying automatic text simplification for the medical domain (Cardon and Grabar, 2020) on the original sentences.…”

Section: Discussionmentioning

confidence: 99%

Extending a Text-to-Pictograph System to French and to Arasaac

Norré¹,

Vandeghinste²,

Bouillon

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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We present an adaptation of the Text-to-Picto system, initially designed for Dutch, and extended to English and Spanish. The original system, aimed at people with an intellectual disability, automatically translates text into pictographs (Sclera and Beta). We extend it to French and add a large set of Arasaac pictographs linked to WordNet 3.1. To carry out this adaptation, we automatically link the pictographs and their metadata to synsets of two French WordNets and leverage this information to translate words into pictographs. We automatically and manually evaluate our system with different corpora corresponding to different use cases, including one for medical communication between doctors and patients. The system is also compared to similar systems in other languages.

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Section: Discussionmentioning

confidence: 99%

Extending a Text-to-Pictograph System to French and to Arasaac

Norré¹,

Vandeghinste²,

Bouillon

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…All the existing large corpora used post-hoc aligned sentences [30,2,33,32,3]. The SimpleText corpus [11] contains directly simplified sentences, and is not much smaller than existing high-quality corpora like NEWSELA [30] (2,259 sentences).…”

Section: Task 3: Text Simplification -Rewriting Scientific Textmentioning

confidence: 99%

CLEF 2023 SimpleText Track

Ermakova

SanJuan

Huet

et al. 2023

Lecture Notes in Computer Science

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The general public tends to avoid reliable sources such as scientific literature due to their complex language and lacking background knowledge. Instead, they rely on shallow and derived sources on the web and in social media -often published for commercial or political incentives, rather than the informational value. Can text simplification help to remove some of these access barriers? This paper presents the CLEF 2023 SimpleText track tackling technical and evaluation challenges of scientific information access for a general audience. We provide appropriate reusable data and benchmarks for scientific text simplification, and promote novel research to reduce barriers in understanding complex texts. Our overall use-case is to create a simplified summary of multiple scientific documents based on a popular science query which provides a user with an accessible overview on this specific topic. The track has the following three concrete tasks. Task 1 (What is in, or out? ): selecting passages to include in a simplified summary. Task 2 (What is unclear? ): difficult concept identification and explanation. Task 3 (Rewrite this! ): text simplification -rewriting scientific text. The three tasks together form a pipeline of a scientific text simplification system.

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“…Shardlow and Nawaz (2019) used the general purpose neural text simplification model (Nisioi et al, 2017) augmented with the phrase table of complex-simple medical terminology to automatically simplify clinical letters in English. Cardon and Grabar (2020) used similar approach for biomedical texts in French. Emphasizing the need for high-quality simplification in medical domain, Van et al (2020) explored the possibility of applying pretrained neural language models to the autocomplete process for sentencelevel medical text simplification.…”

Section: Ats In Medical Domainmentioning

confidence: 99%

Automatic Text Simplification for Social Good: Progress and Challenges

Štajner

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Since the late 1990s, automatic text simplification (ATS) was promoted as a natural language processing (NLP) task with great potential to make texts more accessible to people with various reading or cognitive disabilities, and enable their better social inclusion. Large multidisciplinary projects showed promising steps in that direction. Since 2010, the field started attracting more attention but at the cost of major shifts in system architecture, target audience, and evaluation strategies. Somewhere along the way, the focus has shifted from ATS for social good towards building complex endto-end neural architectures that are not aimed at any particular target population. This study presents the trajectory of ATS for social good, the main issues in current ATS trends, and the ways forward that could bring the field back to its initial goals.

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French Biomedical Text Simplification: When Small and Precise Helps

Cited by 10 publications

References 15 publications

Extending a Text-to-Pictograph System to French and to Arasaac

Extending a Text-to-Pictograph System to French and to Arasaac

CLEF 2023 SimpleText Track

Automatic Text Simplification for Social Good: Progress and Challenges

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