###### Abstract  
Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story. text summarization; event detection; machine learning; natural language processing; Twitter +
Footnote †: journal:  
a large number of information sources in real time can help by providing event summaries, which are updated whenever important new events are detected. The update of the event will help those interested persons to understand the situation of the current event. With the real-time nature of social media, an automated event summary system using social media (such as Twitter) data can provide people with event updates while the situation is still evolving, without the need for individuals to manually analyze a large number of news articles. The term \"real-time\" here means that the summary system can expand or update the summary at any point in time, and the real-time computing property is not the focus of this work. In order to meet the above-mentioned increasing demand, in this work, we provide a solution for real-time event summarization. It combines the developed Twitter-based real-time event detection algorithm with a pre-trained language model for summarizing emergency events. Automatically summarizing social message topics is a relatively unnoticed emerging research field. Essentially, tweet summaries can be viewed as an example of a more general automatic text summarization problem, which is an issue of automatically generating a condensed version of the most important content from user-generated text content. In addition, it is also a type of stream summarization. For text streams containing millions of documents covering various topics and events, traditional single-document and multi-document summarization methods cannot solve the challenge of information overload. [PERSON] [1] summarized the methods of automatic summarization on Twitter's most popular topics. For some highly structured and recurring events in social media messages, it is best to use more complex techniques to summarize relevant information. [PERSON] [2] formalized the summary of event messages and proposed a hidden Markov model.