Building a comparable corpus and a benchmark for Spanish medical text simplification

• Autores: Leonardo Campillos Llanos , Ana Rosa Terroba Reinares, Sofía Zakhir Puig, Ana Valverde, Adrián Capllonch Carrión
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 69, 2022, págs. 189-196
• Idioma: inglés
• Títulos paralelos:
  • Construcción de un corpus comparable y un recurso de referencia para la simplificación de textos médicos en español
• Enlaces
  • Texto completo
• Dialnet Métricas: 2 Citas
• Resumen
  • español

    Se describe la recogida del corpus comparable CLARA-MeD, formado por 24 298 pares de textos profesionales y simplificados de dominio médico en lengua española (>96M palabras). Los tipos de textos varían desde prospectos médicos y fichas técnicas de medicamentos (10 211 pares de textos, >82M palabras), resúmenes de revisiones sistemáticas (8138 pares de textos, >9M palabras), resúmenes de información sobre el cáncer (201 pares de textos, >3M palabras) y anuncios de ensayos clínicos (5748 pares de textos, 451 690 palabras). También presentamos el alineamiento de frases técnicas y simplificadas, realizado a mano por pares de anotadores. Un subconjunto de 3800 pares de frases (149 862 tokens) se han emparejado, con un acuerdo medio entre anotadores con valor kappa = 0.839 (0.076). Los datos están disponibles en la comunidad y este nuevo recurso permite desarrollar y evaluar sistemas de simplificación automática de textos médicos.

  • English

    We report the collection of the CLARA-MeD comparable corpus, which is made up of 24 298 pairs of professional and simplified texts in the medical domain for the Spanish language (>96M tokens). Texts types range from drug leaflets and summaries of product characteristics (10 211 pairs of texts, >82M words), abstracts of systematic reviews (8138 pairs of texts, >9M words), cancer-related information summaries (201 pairs of texts, >3M tokens) and clinical trials announcements (5748 pairs of texts, 451 690 words). We also report the alignment of professional and simplified sentences, conducted manually by pairs of annotators. A subset of 3800 sentence pairs (149 862 tokens) has been aligned each by 2 experts, with an average inter-annotator agreement kappa score of 0.839 (0.076). The data are available in the community and contributes with a new benchmark to develop and evaluate automatic medical text simplification systems.

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