Characterization of temporal complementarity: fundamentals for multi-document summarization
Complementarity is a usual multi-document phenomenon that commonly occurs among news texts about the same event. From a set of sentence pairs (in Portuguese) manually annotated with CST (Cross-Document Structure Theory) relations (Historical background and Follow-up) that make explicit the temporal...
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| Autores principales: | , , |
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| Formato: | Artículo publishedVersion |
| Lenguaje: | Portugués Inglés |
| Publicado: |
ALFA: Revista de Linguística
2018
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| Materias: | |
| Acceso en línea: | https://periodicos.fclar.unesp.br/alfa/article/view/9204 http://biblioteca.clacso.edu.ar/gsdl/cgi-bin/library.cgi?a=d&c=br/br-048&d=article9204oai |
| Aporte de: |
| Sumario: | Complementarity is a usual multi-document phenomenon that commonly occurs among news texts about the same event. From a set of sentence pairs (in Portuguese) manually annotated with CST (Cross-Document Structure Theory) relations (Historical background and Follow-up) that make explicit the temporal complementary among the sentences, we identified a potential set of linguistic attributes of such complementary. Using Machine Learning algorithms, we evaluate the capacity of the attributes to discriminate between Historical background and Follow-up. JRip learned a small set of rules with high accuracy. Based on a set of 5 rules, the classifier discriminates the CST relations with 80% of accuracy. According to the rules, the occurrence of temporal expression in sentence 2 is the most discriminative feature in the task. As a contribution, the JRip classifier can improve the performance of the CST-discourse parsers for Portuguese. |
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