Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

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 e...

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Autores principales:	Chung-Hong Lee, Hsin-Chang Yang, Yenming J. Chen, Yung-Lin Chuang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	text summarization event detection machine learning natural language processing Twitter Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c616fd472c6e468eac2828ea7b1c8c75
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spelling	oai:doaj.org-article:c616fd472c6e468eac2828ea7b1c8c752021-11-25T16:32:34ZEvent Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques10.3390/app1122105962076-3417https://doaj.org/article/c616fd472c6e468eac2828ea7b1c8c752021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10596https://doaj.org/toc/2076-3417Recently, 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.Chung-Hong LeeHsin-Chang YangYenming J. ChenYung-Lin ChuangMDPI AGarticletext summarizationevent detectionmachine learningnatural language processingTwitterTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10596, p 10596 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	text summarization event detection machine learning natural language processing Twitter Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	text summarization event detection machine learning natural language processing Twitter Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Chung-Hong Lee Hsin-Chang Yang Yenming J. Chen Yung-Lin Chuang Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
description	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.
format	article
author	Chung-Hong Lee Hsin-Chang Yang Yenming J. Chen Yung-Lin Chuang
author_facet	Chung-Hong Lee Hsin-Chang Yang Yenming J. Chen Yung-Lin Chuang
author_sort	Chung-Hong Lee
title	Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
title_short	Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
title_full	Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
title_fullStr	Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
title_full_unstemmed	Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques
title_sort	event monitoring and intelligence gathering using twitter based real-time event summarization and pre-trained model techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c616fd472c6e468eac2828ea7b1c8c75
work_keys_str_mv	AT chunghonglee eventmonitoringandintelligencegatheringusingtwitterbasedrealtimeeventsummarizationandpretrainedmodeltechniques AT hsinchangyang eventmonitoringandintelligencegatheringusingtwitterbasedrealtimeeventsummarizationandpretrainedmodeltechniques AT yenmingjchen eventmonitoringandintelligencegatheringusingtwitterbasedrealtimeeventsummarizationandpretrainedmodeltechniques AT yunglinchuang eventmonitoringandintelligencegatheringusingtwitterbasedrealtimeeventsummarizationandpretrainedmodeltechniques
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Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

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