| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560482 |
| ISSN: | 1405-5546 |
| Autores: | Linhares Pontes, Elvys1 Huet, Stéphane2 Torres Moreno, Juan Manuel3 Silva, Thiago G. da4 Carneiro Linhares, Andréa5 |
| Instituciones: | 1Université de La Rochelle, Poitou-Charentes. Francia 2University of Avignon, Avignon, Vaucluse. Francia 3Polytechnique Montréal, Montreal, Quebec. Canadá 4Instituto Federal de Educação Ciência e Tecnologia da Paraíba, Joao Pessoa, Paraiba. Brasil 5Universidade Federal do Ceará, Fortaleza, Ceara. Brasil |
| Año: | 2020 |
| Periodo: | Abr-Jun |
| Volumen: | 24 |
| Número: | 2 |
| Paginación: | 897-915 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Multi-Sentence Compression (MSC) aims to generate a short sentence with the key information from a cluster of similar sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes an Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, with the goal of generating more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state of the art for evaluations led on news datasets in three languages: French, Portuguese and Spanish. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. In additional tests, which take advantage of the fact that the length of compressions can be modulated, we still improve ROUGE scores with shorter output sentences. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Multi-sentence compression, Integer linear programming, Word graph, Artificial intelligence |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
