In this paper we compare performance of several heuristics in
\ngenerating informative generic\/query-oriented extracts for
\nnewspaper articles in order to learn how topic prominence affects
\nthe performance of each heuristic. We study how different query
\ntypes can affect the performance of each heuristic and discuss the
\npossibility of using machine learning algorithms to automatically
\nlearn good combination functions to combine several heuristics.
\nWe also briefly describe the design, implementation, and
\nperformance of a multilingual text summarization system
\nSUMMARIST.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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