Evaluating the effectiveness of features and sampling in extractive meeting summarization

Feature-based approaches are widely used in the task of extractive meeting summarization. In this paper, we analyze and evaluate the effectiveness of different types of features using forward feature selection in an SVM classifier. In addition to features used in prior studies, we introduce topic related features and demonstrate that these features are helpful for meeting summarization. We also propose a new way to resample the sentences based on their salience scores for model training and testing. The experimental results on both the human transcripts and recognition output, evaluated by the ROUGE summarization metrics, show that feature selection and data resampling help improve the system performance.