The growth of the Web 2.0 technologies has led to an explosion of social networking media sites. Among them, Twitter is the most popular service by far due to its ease for realtime sharing of information. It collects millions of tweets per day and monitors what people are talking about in the trending topics updated timely. Then the question is how users can understand a topic in a short time when they are frustrated with the overwhelming and unorganized tweets. In this paper, this problem is approached by sequential summarization which aims to produce a sequential summary, i.e., a series of chronologically ordered short subsummaries that collectively provide a full story about topic development. Both the number and the content of sub-summaries are automatically identified by the proposed stream-based and semantic-based approaches. These approaches are evaluated in terms of sequence coverage, sequence novelty and sequence correlation and the effectiveness of their combination is demonstrated.
In REF , authors propose to work on a summarization task which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in Twitter.
Sequential Summarization: A New Application for Timely Updated Twitter Trending Topics
ACL
Computer Science