The discourse-level structure of empirical abstracts: an exploratory study
Information Processing and Management: an International Journal
The identification of important concepts in highly structured technical papers
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
New Methods in Automatic Extracting
Journal of the ACM (JACM)
A Discourse Model for Gist Preservation
SBIA '96 Proceedings of the 13th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Concept identification and presentation in the context of technical text summarization
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
FNDS: a dialogue-based system for accessing digested financial news
Journal of Systems and Software
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We describe and evaluate Sum UM, 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 regeneration 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.