文章基本信息

标题：Evaluating a typology of signals for automatic detection of complementarity
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作者：Jackson Wilke da Cruz Souza ; Ariani Di Felippo
期刊名称：Domínios de Lingu@gem
电子版ISSN：1980-5799
出版年度：2022
卷号：16
期号：4
页码：1517-1543
DOI：10.14393/DL52-v16n4a2022-10
语种：English
出版社：Universidade Federal de Uberlândia
摘要：In a cluster of news texts on the same event, two sentences from different documents might express different multi-document phenomena (redundancy, complementarity, and contradiction). Cross-Document Structure Theory (CST) provides labels to explicitly represent these phenomena. The automatic identification of the multi-document phenomena and their correspondent CST relations is definitely handy for Automatic Multi-Document Summarization since it helps computers understand text meaning. In this paper, we evaluated a typology of (textual) signals for the automatic detection of the CST relations of complementarity (i.e., Historical background, Follow-up and Elaboration) in a multi-document corpus of news texts in Brazilian Portuguese. Using algorithms from different machine-learning paradigms, we obtained classifiers that achieved high general accuracy (higher than 90%), indicating the potential of the signals.