A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
SimSum: an empirically founded simulation of summarizing
Information Processing and Management: an International Journal
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A classification algorithm for predicting the structure of summaries
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
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We investigate the problem of inserting rhetorical predicates (e.g. "to present", "to discuss", "to indicate", "to show") during non extractive summary generation and compare various algorithms for the task which we trained over a set of human written summaries. The algorithms which use a set of features previously introduced in the summarization literature achieve between 57% to 62% accuracy depending on the machine learning algorithm used. We draw conclusions with respect to the use of context during predicate prediction.