Article Structure
Abstract
The growth of the Web 2.0 technologies has led to an explosion of social networking media sites.
Introduction and Background
Twitter, as a popular micro-blogging service, collects millions of real-time short text messages (known as tweets) every second.
Sequential Summarization
Sequential summarization proposed here aims to generate a series of chronologically ordered sub-summaries for a given Twitter trending topic.
Experiments and Evaluations
The experiments are conducted on the 24 Twitter trending topics collected using Twitter APIs 3.
Concluding Remarks
We start a new application for Twitter trending topics, i.e., sequential summarization, to reveal the developing scenario of the trending topics while retaining the order of information presentation.
Topics
cosine similarity
- For a tweet in a peak area, the linear combination of two measures is considered to evaluate its significance to be a sub-summary: (l) subtopic representativeness measured by the cosine similarity between the tweet and the centroid of all the tweets in the same peak area; (2) crowding endorsement measured by the times that the tweet is re-tweeted normalized by the total number of re-tweeting.Page 3, “Sequential Summarization”
- Borrowing this idea, for each sub-summary in a human-generated summary, we find its most matched sub-summary (judged by the cosine similarity measure) in the corresponding system-generated summary and then define the correlation according to the concordance between the twoPage 4, “Experiments and Evaluations”
- For the semantic-based approach, we compare three different approaches to defining the subtopic number K: (1) Semantic-based 1: Following the approach proposed in (Li et al., 2007), we first derive the matrix of tweet cosine similarity .Page 4, “Experiments and Evaluations”
See all papers in Proc. ACL 2013 that mention cosine similarity.
See all papers in Proc. ACL that mention cosine similarity.
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