We introduce a content selection method for opinion summarization based on a well-studied, formal mathematical model, the p-median clustering problem from facility location theory. Our method replaces a series of local, myopic steps to content selection with a global solution, and is designed to allow content and realization decisions to be naturally integrated. We evaluate and compare our method against an existing heuristic-based method on content selection, using human selections as a gold standard. We find that the algorithms perform similarly, suggesting that our content selection method is robust enough to support integration with other aspects of summarization.
Cheung et al. REF proposed a formal optimization-based method for summarization content selection based on the p-median clustering paradigm, in which content selection is viewed as selecting clusters of related information.
Optimization-based Content Selection for Opinion Summarization
Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)
Computer Science