| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560727 |
| ISSN: | 1405-5546 |
| Autores: | Patel, Vishwa1 Tabrizi, Nasseh1 |
| Instituciones: | 1East Carolina University, North Carolina. Estados Unidos |
| Año: | 2022 |
| Periodo: | Jul-Sep |
| Volumen: | 26 |
| Número: | 3 |
| Paginación: | 1259-1267 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The 21st century has become a century of information overload, where in fact information related to even one topic (due to its huge volume) takes a lot of time to manually summarize it into few lines. Thus, in order to address this problem, Automatic Text Summarization methods have been developed. Generally, there are two approaches that are currently being used in practice: Extractive and ive methods. In this paper, we have reviewed papers from IEEE and ACM libraries those related to Automatic Text Summarization for the English language. |
| Keyword: | Automatic text summarization, Extractive, Abstractive, Review paper |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
