Real-Time Summarization of Twitter
This work addresses the problem of filtering and summarizing Twitter streams in real-time for users needing timely updates, but it is incremental as it builds on existing methods without major breakthroughs.
The paper tackles real-time summarization of Twitter for push notifications by using Dirichlet scoring to classify tweet relevance to interest profiles, achieving good performance as measured by MAP, CG, and DCG metrics.
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.