NEWS Article Summarization with Pretrained Transformer

Garg, Apar; Adusumilli, Saiteja; Yenneti, Shanmukha; Badal, Tapas; Garg, Deepak; Pandey, Vivek; Nigam, Aditya; Gupta, Yashu Kant; Mittal, Gyan; Agarwal, Rahul

doi:10.1007/978-981-16-0401-0_15

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kerui [42] uses BERT, Seq2seq and reinforcement learning to form a text summary model. Garg [43] uses T5, one of the most advanced pre-trained models, to perform a summary task on a data set with 80,000 news articles, and the results indicate that the summary generated by T5 has better quality than those generated by other models. Daiya [44] has developed a pre-trained language model ENEMAbst that can be used in extractive and abstractive summarization techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, due to our task requires extensive domain knowledge beyond the meaning of individual words and sentences, in this work we limit our focus to abstractive summarization algorithms. On the other hand, in the recent references of text summarization, there are more and more work using pre-training language models [35][36][37][38][39][40][41][42][43][44]. Among them, BERT is the pre-trained model mainly used by most research teams.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Lee

Yang

Chen³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%