## 3 Models and Methods  
### System Framework  
In this work, the pre-trained models are mainly used to perform summarization function on social media data to fine-tune the text summaries. With the rapid development of social media, many messages flow and gather on social media, forming various event topics. However, identifying \"event\" is one of the most difficult challenges of current decision support systems. If the system encounters an emergency, it is usually unable to promptly notify users of the warning to avoid risks. A sensible solution is to employ the functions of the real-time event summarization to obtain emergent event information. As a result, the system framework of this study utilizes the method of event detection on social-media and messages to extract their main content, and then summarized to understand the evolution of important events. Figure 1 illustrates the conceptual diagram of the process followed to extract real-time event intelligence for both the event detection and summarization. The system is designed as a two-pass process, consisted of two modules, say, real-time event detection module and event-message accumulation and summarization module. More details on real-time event summarization are presented in the following. Firstly, 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 major international events. The unimportant messages on the data set are removed through the trained SBERT based classifier. After that, the DBSCAN algorithm is used to cluster similar topics in each day into hot topics (i.e., event topics). These event topics in Twitter data are generated by using the algorithm we developed to generate storylines. When the hot topics of each day are summarized, the specific topic and other hot topics are semantically correlated. When the correlation exceeds a certain threshold, it is added to the storyline. After daily updates, through the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citing the Author** & **Model** & \
\begin{tabular}{c} **Using Pre-Trained** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Method of Text** \\ **Summarization** \\ \end{tabular} & **Data Set** \\ \hline [PERSON] et al. [23] & CNN-LSTM & No & Extractive & NIKKEI Financial \\  & & & & Report \\ [PERSON] et al. [24] & SummaRuNNer & No & Extractive & CNN/Daily Mail and \\  & & & & DUC 2002 \\ [PERSON] et al. [33] & Bidirectional LSTM & No & Abstractive & Amazon Fine Food \\  & & & & Reviews \\ [PERSON] et al. [45] & BERT & Yes & Abstractive & German Wikipedia \\  & & & & articles \\ [PERSON] et al. [42] & BERT & Yes & Abstractive & LCSTS \\ [PERSON] et al. [43] & T5 & Yes & Abstractive and Extractive & 80 K News Articles \\ [PERSON] et al. [40] & mT5 & Yes & Abstractive & Pn-summary \\ [PERSON] et al. [34] & RC-Transformer & Yes & Abstractive & Gigaword and \\  & & Conditional & & DUC 2004 \\ [PERSON] et al. [29] & Generation by RNN & No & Abstractive & \\ [PERSON] et al. [30] & CNN-LSTM & No & Abstractive & \\ [PERSON] et al. [31] & Attention-RNN & No & Abstractive & \\ [PERSON] et al. [41] & T-BERTSum & Yes & Abstractive & CNN/Daily Mail \\ [PERSON] et al. [39] & HIBERT & Yes & Abstractive & \\ [PERSON] et al. [36] & BERT & Yes & Abstractive and Extractive & \\ [PERSON] [44] & ENEMABst & Yes & Abstractive and Extractive & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work of automated text summarization. Twitter based real-time intelligence data collection, a storyline of a specific event is jointly generated. The next work is to deal with text summarization of specific topics. In this work, we use the pre-trained model (i.e., T5; Text-to Text Transfer Transformer) to experiment with the Xsum data set to perform text summarization, and the Rouge metrics are used to evaluate the resulting summaries.