Training a Selection Function for Extraction
Proceedings of the Eighteenth Annual International ACM Conference on Information and Knowledge Management (CIKM), Kansas City, Kansas |
In this paper we compare performance of several heuristics in
generating informative generic/query-oriented extracts for
newspaper articles in order to learn how topic prominence affects
the performance of each heuristic. We study how different query
types can affect the performance of each heuristic and discuss the
possibility of using machine learning algorithms to automatically
learn good combination functions to combine several heuristics.
We also briefly describe the design, implementation, and
performance of a multilingual text summarization system
SUMMARIST.