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

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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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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Notes

  1. 1.

    http://www.ansi.org.

  2. 2.

    https://www.internetlivestats.com/.

  3. 3.

    https://en.wikipedia.org/wiki/2016_Indian_banknote_demonetisation.

  4. 4.

    https://en.wikipedia.org/wiki/2016_United_States_presidential_election.

  5. 5.

    https://en.wikipedia.org/wiki/Me_Too_movement_(India).

  6. 6.

    https://bit.ly/33lKpTj.

  7. 7.

    https://bit.ly/3dVJrQ4.

  8. 8.

    https://github.com/Twitter4J/Twitter4J.

  9. 9.

    We experimented with the different values of min_cluster_size but with 100 we got the best clustering.

  10. 10.

    shorturl.at/aeOTW.

  11. 11.

    shorturl.at/crtI4.

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Authors and Affiliations

  1. National Institute of Technology Agartala, Agartala, 799046, Tripura, India

    Kunal Chakma & Swapan Debbarma

  2. Wipro AI Lab, Bangalore, India

    Amitava Das

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  1. Kunal Chakma
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  2. Amitava Das
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  3. Swapan Debbarma
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Correspondence to Kunal Chakma .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Chakma, K., Das, A., Debbarma, S. (2021). Summarization of Twitter Events with Deep Neural Network Pre-trained Models. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_4

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

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