Evaluating a typology of signals for automatic detection of complementarity
Bibliographic Data
| ID | 21849549 |
|---|---|
| Authors | Jackson Wilke Da Cruz Souza (0000-0003-1881-6780, Centro Universitário de Formiga), Ariani Di Felippo (0000-0002-4566-9352, Universidade Federal de São Carlos) |
| Year | 2022 |
| Volume | 16 |
| Issue | 4 |
| Pages | 1517-1543 |
| Publication date | 2022-09-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Domínios de Lingu gem (JOURNAL) |
| Journal identifiers | ISSN: 1980-5799 • E-ISSN: 1980-5799 |
| Publisher | PPUFU - Portal de Periódicos da Universidade Federal de Uberlândia (PUBLISHER) |
| DOI | 10.14393/dl52-v16n4a2022-10 |
| OpenAlex | W4297081714 |
| Language | EN |
| References cited | 15 |
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
Automatic summarization · Contradiction · Information retrieval · Linguistics · Natural language processing · Typology · Advanced Text Analysis Techniques · Computer Science · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Highly cited | No |
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