Summarizing User-Contributed Comments

Authors

  • Elham Khabiri Texas A&M University
  • James Caverlee Texas A&M University
  • Chiao-Fang Hsu Texas A&M University

Abstract

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.

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Published

2021-08-03

How to Cite

Khabiri, E., Caverlee, J., & Hsu, C.-F. (2021). Summarizing User-Contributed Comments. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 534-537. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14192