User-contributed content is creating a surge on the Internet. A list of "buzzing topics" can effectively monitor the surge and lead people to their topics of interest. Yet a topic phrase alone, such as "SXSW", can rarely present the information clearly. In this paper, we propose to explore a variety of text sources for summarizing the Twitter topics, including the tweets, normalized tweets via a dedicated tweet normalization system, web contents linked from the tweets, as well as integration of different text sources. We employ the concept-based optimization framework for topic summarization, and conduct both automatic and human evaluation regarding the summary quality. Performance differences are observed for different input sources and types of topics. We also provide a comprehensive analysis regarding the task challenges.
REF proposed to use the concept-based ILP framework for summarizing the Twitter trending topics, using both tweets and the webpages linked from the tweets as input text sources.
Why is "SXSW" trending? Exploring Multiple Text Sources for Twitter Topic Summarization
Proceedings of the Workshop on Language in Social Media (LSM 2011)
Computer Science