  Social media platforms are a rich source of information these days, however,
of all the available information, only a small fraction is of users' interest.
To help users catch up with the latest topics of their interests from the large
amount of information available in social media, we present a relevant content
filtering based framework for data stream summarization. More specifically,
given the topic or event of interest, this framework can dynamically discover
and filter out relevant information from irrelevant information in the stream
of text provided by social media platforms. It then further captures the most
representative and up-to-date information to generate a sequential summary or
event story line along with the evolution of the topic or event. Our framework
does not depend on any labeled data, it instead uses the weak supervision
provided by the user, which matches the real scenarios of users searching for
information about an ongoing event. We experimented on two real events traced
by a Twitter dataset from TREC 2011. The results verified the effectiveness of
relevant content filtering and sequential summary generation of the proposed
framework. It also shows its robustness of using the most easy-to-obtain weak
supervision, i.e., trending topic or hashtag. Thus, this framework can be
easily integrated into social media platforms such as Twitter to generate
sequential summaries for the events of interest. We also make the manually
generated gold-standard sequential summaries of the two test events publicly
available for future use in the community.
