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Comparative Analysis of Question Answering Models for HRI Tasks with NAO in Spanish Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Summarization of Twitter Events with Deep Neural Network Pre-trained Models

Authors : Kunal Chakma, Amitava Das, Swapan Debbarma

Published in: Information Management and Big Data

Publisher: Springer International Publishing

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Abstract

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.

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Footnotes
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We experimented with the different values of min_cluster_size but with 100 we got the best clustering.
 
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shorturl.at/aeOTW.
 
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shorturl.at/crtI4.
 
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Metadata
Title
Summarization of Twitter Events with Deep Neural Network Pre-trained Models
Authors
Kunal Chakma
Amitava Das
Swapan Debbarma
Copyright Year
2021
Book
Information Management and Big Data

Print ISBN: 978-3-030-76227-8

Electronic ISBN: 978-3-030-76228-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-76228-5_4

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