Characterization of temporal complementarity: fundamentals for multi-document summarization

• Autor(a) principal: Souza, Jackson Wilke da Cruz
• Data de Publicação: 2018
• Outros Autores: Felippo, Ariani Di
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Alfa (São José do Rio Preto. Online)
• Texto Completo: https://periodicos.fclar.unesp.br/alfa/article/view/9204
• topic: Linguistic description; Complementarity; CST; Multi-document Summarization; Natural Language Processing; Descrição linguística; Complementaridade; CST; Sumarização Multidocumento; Processamento Automático de Língua Natural

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.