| Abstract | We applied a single-document sentence-trimming approach (Trimmer) to the
problem of multi-document summariza-
tion. Trimmer was designed with the in-
tention of compressing a lead sentence
into a space consisting of tens of char-
acters. In our Multi-Document Trimmer
(MDT), we use Trimmer to generate
multiple trimmed candidates for each
sentence. Sentence selection is used
to determine which trimmed candidates
provide the best combination of topic
coverage and brevity. We demonstrate
that we were able to port Trimmer easily
to this new problem. We also show that
MDT generally ranked higher for recall
than for precision, suggesting that MDT
is currently more successful at finding
relevant content than it is at weeding out
irrelevant content. Finally, we present
an error analysis that shows that, while
sentence compressions is making space
for additional sentences, more work is
needed in the area of generating and se-
lecting the right candidates.
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