2013 IEEE 13th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.50
OCCAMS is a new algorithm for the Multi-Document Summarization (MDS) problem. We use Latent Semantic Analysis (LSA) to produce term weights which identify the main theme(s) of a set of documents. These are used by our heuristic for extractive sentence selection which borrows techniques from combinatorial optimization to select a set of sentences such that the combined weight of the terms covered is maximized while redundancy is minimized. OCCAMS outperforms CLASSY11 on DUC/TAC data for nearly all years since 2005, where CLASSY11 is the best human-rated system of TAC 2011. OCCAMS also delivers higher ROUGE scores than all human-generated summaries for TAC 2011. We show that if the combinatorial component of OCCAMS, which computes the extractive summary, is given true weights of terms, then the quality of the summaries generated outperforms all human generated summaries for all years using ROUGE-2, ROUGE-SU4, and a coverage metric. We introduce this new metric based on term coverage and demonstrate that a simple bi-gram instantiation achieves a statistically significant higher Pearson correlation with overall responsiveness than ROUGE on the TAC data.
Approximation methods, Approximation algorithms, Entropy, Semantics, Optimization, Humans, Redundancy, Latent Semantic Analysis, Multi-document Summarization, Combinatorial Optimization
Sashka T. Davis, John M. Conroy, Judith D. Schlesinger, "OCCAMS -- An Optimal Combinatorial Covering Algorithm for Multi-document Summarization", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 454-463, 2012, doi:10.1109/ICDMW.2012.50