Multiple View Summarization Framework for Social Media

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

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.

Identifier

85161387425 (Scopus)

Publication Title

Proceedings of the International Florida Artificial Intelligence Research Society Conference Flairs

External Full Text Location

https://doi.org/10.32473/flairs.36.133169

e-ISSN

23340762

ISSN

23340754

Volume

36

Grant

UL1TR003017

Fund Ref

National Institutes of Health

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