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We present results of an empirical study of the usefulness of
different types of features in selecting extractive summaries of news
broadcasts for our Broadcast News Summarization System. We evaluate
lexical, prosodic, structural and discourse features as predictors of
those news segments which should be included in a summary. We show
that a summarization system that uses a combination of these feature
sets produces the most accurate summaries, and that a combination of
acoustic/prosodic and structural features are enough to build a `good'
summarizer when speech transcription is not available.
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