# abstract
<jats:p>                User-contributed comments are one of the hallmarks of the Social Web, widely adopted across social media sites and mainstream news providers alike. While comments encourage higher-levels of user engagement with online media, their wide success places new burdens on users to process and assimilate the perspectives of a huge number of user-contributed perspectives. Toward overcoming this problem we study in this paper the comment summarization problem: for a set of n user-contributed comments associated with an online resource, select the best top-k comments for summarization. In this paper we propose (i) a clustering-based approach for identifying correlated groups of comments; and (ii) a precedence-based ranking framework for automatically selecting informative user-contributed comments. We find that in combination, these two salient features yield promising results.            </jats:p>

# publicationOutlet
Proceedings of the International AAAI Conference on Web and Social Media

# nameOfConference
Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July 17-21, 2011

# publisher.label
Association for the Advancement of Artificial Intelligence (AAAI)

# editorList[]
Adamic, L. A., Baeza-Yates, R., & Counts, S.