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Summary: Multiple Alternative Sentence Compressions
for Automatic Text Summarization
Nitin Madnani, David Zajic, Bonnie Dorr, Necip Fazil Ayan & Jimmy Lin
Institute for Advanced Computer Studies
University of Maryland
College Park, MD, 20742
{nmadnani,dmzajic,bonnie,nfa,jimmylin}@umiacs.umd.edu
Abstract
We perform multi-document summariza-
tion by generating compressed versions
of source sentences as summary candi-
dates and using weighted features of these
candidates to construct summaries. We
combine a parse-and-trim approach with
a novel technique for producing multiple
alternative compressions for source sen-
tences. In addition, we use a novel method
for tuning the feature weights that maxi-
mizes the change in the ROUGE-2 score
(ROUGE) between the already existing
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