  Online social media platforms are turning into the prime source of news and
narratives about worldwide events. However,a systematic summarization-based
narrative extraction that can facilitate communicating the main underlying
events is lacking. To address this issue, we propose a novel event-based
narrative summary extraction framework. Our proposed framework is designed as a
probabilistic topic model, with categorical time distribution, followed by
extractive text summarization. Our topic model identifies topics' recurrence
over time with a varying time resolution. This framework not only captures the
topic distributions from the data, but also approximates the user activity
fluctuations over time. Furthermore, we define significance-dispersity
trade-off (SDT) as a comparison measure to identify the topic with the highest
lifetime attractiveness in a timestamped corpus. We evaluate our model on a
large corpus of Twitter data, including more than one million tweets in the
domain of the disinformation campaigns conducted against the White Helmets of
Syria. Our results indicate that the proposed framework is effective in
identifying topical trends, as well as extracting narrative summaries from text
corpus with timestamped data.
