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  • 标题:Evaluating a typology of signals for automatic detection of complementarity
  • 本地全文:下载
  • 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.
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