CHARACTERIZATION OF TEMPORAL COMPLEMENTARITY: FUNDAMENTALS FOR MULTI-DOCUMENT SUMMARIZATION

ABSTRACT 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

摘要 补全性是一类常见的多文档现象，常出现在围绕同一事件的新闻文本中。本研究以一批经人工标注了跨文档结构理论（Cross-Document Structure Theory, CST）关系（历史背景与后续事件）的葡萄牙语句对为数据源，这类关系明确了句子间的时间补全关联，我们借此挖掘出了此类补全现象的潜在语言属性集合。随后，我们借助机器学习算法，评估了这些属性对区分历史背景与后续事件类CST关系的能力。其中JRip算法学习得到了一组规模极小且准确率优异的规则；基于这5条规则构建的分类器，能够以80%的准确率区分上述CST关系。分析规则可知，第2句中时间表达的出现是该任务中最具区分度的特征。本研究的一项贡献在于，该JRip分类器可提升面向巴西葡萄牙语的CST话语解析器的性能。