Abstract
We describe and evaluate SumUM, a text summarization system that produces indicative-informative abstracts for technical papers. Our approach consists of the shallow syntactic and conceptual analysis of the source document and of the implementation of text re-generation techniques based on a study of abstracts produced by professional abstractors. In an evaluation of indicative content in a categorization task, we observed no differences with other automatic method, while differences are observed in an evaluation of informative content. In an evaluation of text quality, the abstracts were considered acceptable when compared with other automatic abstracts.
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Saggion, H., Lapalme, G. (2000). Summary Generation and Evaluation in SumUM. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_34
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DOI: https://doi.org/10.1007/3-540-44399-1_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41276-2
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