Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization

We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimizationbased extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic Pyramid as the fitness function. Our experimental evaluation shows that our new framework significantly outperforms strong baselines regarding automatic Pyramid, and that there is much room for improvement in comparison with the upperbound for automatic Pyramid.