46 research outputs found
Multi-Document Summarization via Discriminative Summary Reranking
Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models can always produce high-quality summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach
Constrained Submodular Maximization: Beyond 1/e
In this work, we present a new algorithm for maximizing a non-monotone
submodular function subject to a general constraint. Our algorithm finds an
approximate fractional solution for maximizing the multilinear extension of the
function over a down-closed polytope. The approximation guarantee is 0.372 and
it is the first improvement over the 1/e approximation achieved by the unified
Continuous Greedy algorithm [Feldman et al., FOCS 2011]