## 6 Discussion  
In this work we use natural language processing techniques to deal with social media data, extract event topics from daily social messages, and summarize them to quickly grasp various event intelligence information. In the experiment, we adopted a pre-trained model (i.e., T5) method to quickly fine-tune the model to learn text summaries through the Xsum data set, which was achieved a score of 38.13 in the evaluation of Rouge-1. After that, the trained model is actually applied to real social media data. We started to use the trained model to summarize the incoming Twitter data set for experimentation. The model summarizes the collected hot topics of social media into a short sentence. The resulting summary can help users quickly understand topic trends on social media, most of which are in line with the development of real-world events. Furthermore, in order to verify the quality of the text summary results, we conducted a survey on the resulting summaries with human judges for quality assessment of generated summaries. The experimental result is discussed in detail as follows:  
1. The T5 model is trained in an end-to-end and text-to-text manner, with text as input and summary text as output. In this work, we demonstrated that T5 has achieved state-of-the-art results in many NLP benchmarks and can be fine-tuned to perform automatic summarization tasks well. 2. As mentioned earlier, due to the real-time nature of social media, an automatic event summary system using Twitter data was developed in this work to provide people with an updated summary of the event while the situation is still evolving. However, it is worth mentioning that the \"real-time\" here means that the summary system can expand or update the summary at any point in time, rather than real-time computing. 3. Going further, \"real-time event summarization\" is different from \"real-time event detection\" methods. The summarization system we developed can summarize the continuously accumulated hot topic tweets into a short sentence, instead of just performing real-time event detection. Compared to real-time event detection methods, detected events on Twitter are often short-term. The summary generated by our system can represent a simplified version of the event tweet message of interest, starting at any point in time of the ongoing event. 4. Generally speaking, abstractive summarization models are prone to produce grammatically correct incoherent sentences. In the work of summarizing text, it is easy for human researchers to make language errors. The advantages of using abstractive summarization models enable us to alleviate some of the grammatical problems of real people. Despite the results of the system evaluation show that the summaries we got are not perfect in terms of grammatical fluency, the results are acceptable and close to the performance of human researchers in the real world. 5. Although this developed model can quickly summarize the text, it still has certain limitations in generality. The social media data used in this study only uses English data on Twitter to generate summaries. Although English is the common language of most people in the world, it must be only through English to understand the various events in the world, there are still some prejudices. In the future, we consider using multilingual models and multilingual data sets (made by different native speakers) to train the model so that we can more accurately grasp various world events from different perspectives. 6. In this work, we implemented a method of generating storylines using the automatic summarization technique we developed. The generated storyline is composed of sub-events or key events related to a given major event. The sub-events show the status of the main event in progress. Automated storyline generation has been a research issue since nearly the inception of artificial intelligence. In particular, the storylines of events relate to identify entities and summarize events that leads to the event of interest.