Summarization of Twitter Events with Deep Neural Network Pre-trained Models
| Autor: | Swapan Debbarma, Amitava Das, Kunal Chakma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Event (computing) business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Automatic summarization Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering Social media Artificial intelligence Cluster analysis Representation (mathematics) business computer Natural language processing 0105 earth and related environmental sciences Transformer (machine learning model) |
| Zdroj: | Information Management and Big Data ISBN: 9783030762278 SIMBig |
| DOI: | 10.1007/978-3-030-76228-5_4 |
| Popis: | Due to the proliferation of online social media services such as Twitter, there is an upsurge in the volume of user-generated textual content. Such voluminous content is difficult to be consumed by users. Therefore, the development of technological solutions to automatically summarise the voluminous texts are essential. The work presented in this paper reports on the development of automatically generating abstractive summaries from a collection of texts from Twitter. Our proposed approach is a two-stage framework which includes: 1) Event detection by clustering and 2) Summarization of the events. We first generated a contextualized vector representation of the tweets and then applied different clustering techniques on the vectors. We evaluated the generated clusters, and based on the evaluation; we chose the best one found suitable for the summarization task. For the summarization task, we used the pre-trained models of two recently developed state-of-the-art deep neural network architectures and evaluated them on the event clusters. Standard measures of ROUGE scores have been used for evaluating the summaries. We obtained best ROUGE-1 score of 46%, ROUGE-2 score of 30%, ROUGE-L score of 41% and ROUGE-SU score of 23% from our experiments. |
| Databáze: | OpenAIRE |
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