We study the use of temporal information
in the form of timelines to enhance multidocument
summarization. We employ a
fully automated temporal processing system
to generate a timeline for each input
document. We derive three features
from these timelines, and show that their
use in supervised summarization lead to a
significant 4.1% improvement in ROUGE
performance over a state-of-the-art baseline.
In addition, we propose TIMEMMR,
a modification to Maximal Marginal Relevance
that promotes temporal diversity
by way of computing time span similarity,
and show its utility in summarizing
certain document sets. We also propose a
filtering metric to discard noisy timelines
generated by our automatic processes, to
purify the timeline input for summarization.
By selectively using timelines guided
by filtering, overall summarization performance
is increased by a significant 5.9%.