  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.
