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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Bibliographic Details
Published in:Alfa 2018-01, Vol.62 (1), p.121-147
Main Authors: Souza, Jackson Wilke da Cruz, Di Felippo, Ariani
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Brazilian Portuguese
Lengua portuguesa
Linguistics
Lingüística
Machine learning
Natural language processing
Sentences
Sintaxis
Summarization
Syntax
Temporalidad
Temporality
Text analysis
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Summary: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 Brazilian Portuguese
ISSN:0002-5216
1981-5794
DOI:10.1590/1981-5794-1804-6