No abstract

Availability of corpora with technical and simplified contents is crucial for the development and test of methods for text simplification. We describe this kind of corpus for the French medical language. The corpus contains texts from three sources: encyclopedia, drug leaflets and scientific summaries. Each source proposes comparable information in specialized and plain languages. A subset of this corpus has been processed manually in order to find and align parallel sentences. This subset currently contains 663 pairs with parallel sentences. Alignment has been done by two annotators and shows 0.76 inter-annotator agreement. The corpus with comparable data is available for research (http://natalia.

We present experiments on biomedical text simplification in French. We use two kinds of corpora -parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French -and a lexicon that associates medical terms with paraphrases. Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences. We evaluate the results with BLEU, SARI and Kandel scores. The results point out that little specialized data helps significantly the simplification.

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 simplification can be done at different levels (lexical, syntactic, semantic, stylistic...) and relies on the corresponding knowledge and resources (lexicon, rules...). Our objective is to propose methods and material for the creation of transformation rules from a small set of parallel sentences differentiated by their technicity. We also propose a typology of transformations and quantify them. We work with French-language data related to the medical domain, although we assume that the method can be exploited on texts in any language and from any domain.

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 exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information. In the biomedical field, simplification may permit patients to understand medical and health texts. Yet, there is currently no such available resources. We propose to exploit comparable corpora which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are in French and are related to the biomedical area. Manually created reference data show 0.76 inter-annotator agreement. Our purpose is to state whether a given pair of specialized and simplified sentences is parallel and can be aligned or not. We treat this task as binary classification (alignment/nonalignment). We perform experiments with a controlled ratio of imbalance and on the highly unbalanced real data. Our results show that the method we present here can be used to automatically generate a corpus of parallel sentences from our comparable corpus.