Machine Learning of Generic and User-Focused Summarization
arXiv:cs/9811006
Abstract
A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.
In Proceedings of the Fifteenth National Conference on AI (AAAI-98), p. 821-826