Abstract
Twitter new event detection aims to identify first
stories in a tweet stream. Typical approaches consider two sub tasks. First, it is necessary to filter
out mundane or irrelevant tweets. Second, tweets
are grouped automatically into event clusters. Traditionally, these two sub tasks are processed separately, and integrated under a pipeline setting, despite that there is inter-dependence between the two
tasks. In addition, one further related task is summarization, which is to extract a succinct summary for representing a large group of tweets. Summarization is related to detection, under the new
event setting in that salient information is universal between event representing tweets and informative event summaries. In this paper, we build
a joint model to filter, cluster, and summarize the
tweets for new events. In particular, deep representation learning is used to vectorize tweets, which
serves as basis that connects tasks. A neural stacking model is used for integrating a pipeline of different sub tasks, and for better sharing between the
predecessor and successors. Experiments show that
our proposed neural joint model is more effective
compared to its pipeline baseline