  In this paper, we describe our approaches to TREC Real-Time Summarization of
Twitter. We focus on real time push notification scenario, which requires a
system monitors the stream of sampled tweets and returns the tweets relevant
and novel to given interest profiles. Dirichlet score with and with very little
smoothing (baseline) are employed to classify whether a tweet is relevant to a
given interest profile. Using metrics including Mean Average Precision (MAP,
cumulative gain (CG) and discount cumulative gain (DCG), the experiment
indicates that our approach has a good performance. It is also desired to
remove the redundant tweets from the pushing queue. Due to the precision limit,
we only describe the algorithm in this paper.
