Multiple View Summarization Framework for Social Media

Chih Yuan Li, Soon Ae Chun, James Geller

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Social Media provide voluminous posts about current topics and events. When a user desires to investigate a popular topic, it is not feasible as there are many posts. Besides, posts show different biases, viewpoints, perspectives, and emotions. Thus, providing summaries of large post sets with different viewpoints is necessary. We develop a multiple view summa-rization framework to generate different view-based summar-ies of Twitter posts. Users can apply different methods to generate summaries: 1) Entity-centered, 2) Social feature-based, 3) Event-based summarization, using all triple embed-dings and 4) Sentiment-based summarization to generate summaries of positive or negative views of tweets. These summarization methods are compared with BertSum, SBert, T5, and Bart-Large-CNN with a gold standard dataset. Our results, based on Rouge scores, were better than these pub-lished extractive and abstractive summarization models.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software


  • COVID-19 Vaccine Tweet Summarization
  • Microblogging Summarization
  • Multiple-View Summarization
  • Sentiment-based summarization
  • Social feature-based summarization

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