## 7 Conclusions  
The uncertainty of emergencies and natural disasters may affect companies and everyone's lives. During such high-impact events, people may want to stay up-to-date as the event develops because they want to better understand the situation and possible trend of the event. In this case, it is almost impossible for anyone to browse through all of the news articles to understand the event stories required for decision-making. Automated summarization systems that access a large number of information sources in real time can help by providing event summaries, which are updated whenever important sub-events are detected. The update of the event will help those interested persons to understand the situation of the current event. In this work, we developed a hybrid method to combine a real-time event summarization system and a self-adaptive Twitter-based event detection method, using pre-trained language models on the tweet data set for online machine learning. Hence, each user can obtain real-time information about specific incidents (or events) and the agility to make appropriate decisions quickly. The goal of this work can be divided into the following two parts: (1). In a high-impact event, people may want to learn the latest information as the event develops. It is necessary to develop an effective real-time event summary system to obtain sufficient real-time intelligence information for effective decision-making; (2). In order to acquire extensive coverage of event details, using pre-trained language models can help us expand more knowledge and trending topics about how event stories develop. Our research is intended as a starting point for exploring the benefits of this potential for event awareness. The novelty and contributions of the work are summarized as follows:  
* We implemented an end-to-end and text-to-text summarization system model, in which text streams as input and summary text as output. * Our system model combined a real-time event summarization system and a self-adaptive Twitter-based event detection method. The developed summarization system can summarize the continuously accumulated hot topic tweets into a short sentence, instead of just performing real-time event detection. * The developed system used pre-trained language models to help users obtain more knowledge and trending topics about how event stories develop. * The system can support users to obtain sufficient real-time intelligence information in specific events to make effective decisions. For future work, it will be interesting to further incorporate the sentiment of the tweet into the summary of related events to predict the world market trends affected by the event. On the other hand, as mentioned earlier, building a summary system in the \"automatic storyline generation\" paradigm is very useful for understanding the details of event development. In this way, it is possible to explore the evolution of a specific event entity in the event story and the relationship between the entity and its event. In addition, by taking advantage of the potential of pre-trained language models, it will be very useful to gather intelligence on multilingual event summaries from multilingual social media messages. Finally, the use of automatic video summarization methods to extend the developed real-time text summarization methods to generate event summaries is also a promising application. **Author Contributions:** Conceptualization, C.-H.L. and Y.-L.C. ; methodology, C.-H.L. ; software, Y.-L.C. ; validation, H.-C.Y., Y.J.C. and Y.-L.C. ; formal analysis, H.-C.Y. ; investigation, C.-H.L. ; resources, Y.J.C. ; data curation, H.-C.Y. ; writing--original draft preparation, C.-H.L. ; writing--review and editing, C.-H.L. ; visualization, Y.-L.C. ; supervision, C.-H.L. ; project administration, C.-H.L. ; funding acquisition, C.-H.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable.