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
The decomposition of human-written summary sentences
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Summary Generation and Evaluation in SumUM
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
Learning predicate insertion rules for document abstracting
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
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We describe a method of text summarization that produces indicative-informative abstracts for technical papers. The abstracts are generated by a process of conceptual identification, topic extraction and re-generation. We have carried out an evaluation to assess indicativeness and text acceptability relying on human judgment. The results so far indicate good performance in both tasks when compared with other summarization technologies.