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: Multilingual Multi-Document Summarization aims at ranking the sentences of a cluster with (at least) 2 news texts (1 in the user’s language and 1 in a foreign language), and select the top-ranked sentences for a summary in the user’s language. We explored three concept-based statistics and one superficial strategy for sentence ranking. We used a bilingual corpus (Brazilian Portuguese-English) encoded in UNL ( Universal Network Language ) with source and summary sentences aligned based on content overlap. Our experiment shows that “concept frequency normalized by the number of concepts in the sentence” is the measure that best ranks the sentences selected by humans. However, it does not outperform the superficial strategy based on the position of the sentences in the texts. This indicates that the most frequent concepts are not always contained in first sentences, usually selected by humans to build the summaries because they convey the main information of the collection. Keywords: content selection; concept; statistical measure; multilingual corpus; multi-document summarization. Keywords : content selection; concept; statistical measure; multilingual corpus; multi-document summarization. Resumo : O objetivo da Sumarizacao Automatica Multilingue Multidocumento e ranquear as sentencas de uma colecao com ao menos duas noticias (1 na lingua do usuario e 1 em lingua estrangeira) e selecionar as mais bem pontuadas para compor um sumario na lingua do usuario. Exploramos tres estatisticas conceituais e uma estrategia superficial para criar um ranque das sentencas quanto a relevância. Para tanto, utilizamos um corpus bilingue (portugues-ingles) anotado via UNL (Universal Network Language) e com textos-fonte e sumarios alinhados em nivel sentencial. A avaliacao indica que a estatistica denominada frequencia de conceitos normalizada pelo numero de conceitos da sentenca e a que melhor reproduz o ranqueamento humano. Essa medida, entretanto, nao supera a estrategia superficial baseada na posicao das sentencas. Isso indica que os conceitos mais frequentes do cluster nem sempre estao contidos nas primeiras sentencas dos textosfonte, usualmente selecionadas pelos humanos para compor os sumarios porque veiculam a informacao principal da colecao. Palavras-chave : selecao de conteudo; conceito; medida estatistica; corpus multilingue; sumarizacao multidocumento.

Exploring content selection strategies for Multilingual Multi-Document Summarization based on the Universal Network Language (UNL)