Jean-Francois Delannoy, Ken Barker, Terry Copeck, Martin Laplante, Stan Matwin and Stan Szpakowicz
Our project, initiated in 1997, approaches text summarization as a knowledge-scant task of passage selection. Several features make this task more discriminating. These features include "smart" key phrase selection that uses machine learning techniques and simple linguistic criteria; dynamic passage selection; adaptation to the type of text; and choice among several styles of summary. This paper presents the guiding principles of the project, describes the current state of the prototype, and discusses short-term and long-term future research.