Perspective-based Microblog Summarization

Social media allows people to express and share a variety of users’ experiences, opinions, beliefs, interpretations, or viewpoints on a single topic. Summarizing a collection of social media textposts (microblogs) on one topic may be challenging and can result in an incoherent summary due to these multiple perspectives by different users. We introduce an approach of microblog summarization based on user perspectives, called Multiple View Summarization Framework (MVSF), a novel approach designed to efficiently generate multiple summaries from the same social media dataset depending on a chosen perspective, delivering personalized and fine-grained summaries. The MVSF leverages component-of-perspective computing that can recognize the perspectives expressed in microblogs, such as sentiments, critical views, political orientations, or unreliable opinions (fake news), etc. The perspective computing can filter social media data to summarize them according to a specific user-selected perspective. For the summarization methods, our framework implements three extractive summarization methods: Entity-based, Social Signal-based, and Triple-based. We conduct comparative evaluations of MVSF summarizations against state-of-the-art summarization models, including BertSum, SBert, T5, and Bart-Large-CNN, by using a gold standard BBC news dataset and Rouge scores. Furthermore, we utilized a dataset of 18,047 tweets about COVID-19 vaccines to demonstrate the applications of MVSF. Our contributions include the innovative approach of using user perspectives in summarization methods as a unified framework, capable of generating multiple summaries that reflect different perspectives, in contrast to prior approaches of one summary for one dataset. The practical implication of MVSF is that it offers end-users diverse perspectives from social media data. Our prototype web application is also implemented using ChatGPT to show the feasibility of our approach.