## 2 Related Work  
In this work, we attempt to build a real-time event summarization system which can generate a simplified version of a dynamically changing event data from the collected Twitter data set. When users read the generated sentences, they will be able to immediately capture the most important part of the latest event content in real time to make appropriate decisions. Using Twitter for event detection and event summarization is a vast subject. Twitter is also employed to better understand how the communication transfers during citizen movements and events. In our previous work [5; 6] a real-time event detection approach has been well developed to gather tweets of detected events for awareness of emerging events in the real world. Summarization of a single text summarization is the task of automatically generating a shorter version of a document and retaining its most important information. The types of automatic text summarization can be classified as extractive and abstractive. The extractive text summarization extracts sentences from the texts, while the abstractive ones may produce sentences and phrases which do not appear in the original documents. In practice, the techniques of abstractive text summarization emulate human summarization in that it utilizes a vocabulary beyond the specified text and summarizes key points in the text. Due to such approaches emulate how people summarize material, they normally require more computing power such as several GPUs to train the model over many days for learning algorithms, thus it is generally more difficult to acquire a cost-effective solution. In this work, we concentrate on abstractive summarization techniques. While text summarization has been investigated for years, automatic summarization on social messages (e.g., tweets) is still in its infancy. [PERSON] et al., used the Phrase Reinforcement algorithm to deal with tweet summarization [7]. [PERSON] developed a Hybrid TF-IDF algorithm to produce multiple post summaries [8]. In order to make our model to summarize text accurately, it needs to learn to \"understand\" the input data. Google's BERT (Bidirectional Encoder Representations from Transformers) is the perfect tool for natural language understanding. Before the emergence of the BERT model, several important techniques related to the modeling method we adopted were developed. Recently, the Recurrent Neural Networks (RNNNs; including Long Short Term Memory Networks) have been widely used for many natural language processing applications. However, it needs massive amounts of data, costly data sources, and several hours of training to achieve satisfactory results. Due to the drawback of suffering from poor performance with very long sequences of data, [PERSON] proposed a model called the \"Transformer\", replacing the RNNs and Convolutional Neural Networks (CNNs) by utilizing an architecture comprised of feed forward networks and attention mechanisms [9]. An unsupervised learning architecture called BERT (Bidirectional Encoder Representations from Transformers) has been developed and outperformed almost all existing models in the competitions for various types of NLP tasks [10]. In addition, they published several pre-trained models which can be utilized for transfer learning for many tasks and applications [10]. Text summarization is a technology that extracts the important text or phrase information from the original text and then presents a short message to the user. According to the content and method of abstracting, it can be divided into Extractive and Abstractive. Extractive summarization is a technique that uses statistics that extracts the most important words and phrases in a text according to the given weight, and then arranges and combines these features to generate a summary of the text; abstractive summarization is a technique that uses machine learning methods to generate a summary of the text after understanding the content of the text [11; 12; 13; 14].