A Learning-based Sampling Approach to Extractive Summarization

In this paper we present a novel resampling model for extractive meeting summarization. With resampling based on the output of a baseline classifier, our method outperforms previous research in the field. Further, we compare an existing resampling technique with our model. We report on an extensive series of experiments on a large meeting corpus which leads to classification improvement in weighted precision and f-score.