Extractive Summarization by Maximizing Semantic Volume
Dani Yogatama, Fei Liu, Noah A. Smith · 2015
The most successful approaches to extrac-tive text summarization seek to maximize bigram coverage subject to a budget con-straint. In this work, we propose instead to maximize semantic volume. We em-bed each sentence in a semantic space and construct a summary by choosing a sub-set of sentences whose convex hull max-imizes volume in that space. We provide a greedy algorithm based on the Gram-Schmidt process to efficiently perform volume maximization. Our method out-performs the state-of-the-art summariza-tion approaches on benchmark datasets. 1