research-article

TSSuBERT: How to Sum Up Multiple Years of Reading in a Few Tweets

Authors:

Karen Pinel-Sauvagnat,

Gilles HubertAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 109, Pages 1 - 33

https://doi.org/10.1145/3581786

Published: 10 April 2023 Publication History

Abstract

The development of deep neural networks and the emergence of pre-trained language models such as BERT allow to increase performance on many NLP tasks. However, these models do not meet the same popularity for tweet stream summarization, which is probably because their computation limitation requires to drastically truncate the textual input.

Our contribution in this article is threefold. First, we propose a neural model to automatically and incrementally summarize huge tweet streams. This extractive model combines in an original way pre-trained language models and vocabulary frequency based representations to predict tweet salience. An additional advantage of the model is that it automatically adapts the size of the output summary according to the input tweet stream. Second, we detail an original methodology to construct tweet stream summarization datasets requiring little human effort. Third, we release the TES 2012-2016 dataset constructed using the aforementioned methodology. Baselines, oracle summaries, gold standard, and qualitative assessments are made publicly available.

To evaluate our approach, we conducted extensive quantitative experiments using three different tweet collections as well as an additional qualitative evaluation. Results show that our method outperforms state-of-the-art ones. We believe that this work opens avenues of research for incremental summarization, which has not received much attention yet.

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Cited By

Ehnert PSchröter J(2024)Key point generation as an instrument for generating core statements of a political debate on TwitterFrontiers in Artificial Intelligence10.3389/frai.2024.12009497Online publication date: 20-Mar-2024
https://doi.org/10.3389/frai.2024.1200949
Paul DRana SSaha SMathew J(2024)Online Summarization of Microblog Data: An Aid in Handling Disaster SituationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334752011:3(4029-4039)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3347520
Garg PChakraborty RDandapat S(2024)OntoDSumm: Ontology-Based Tweet Summarization for Disaster EventsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326602511:2(2724-2739)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266025
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Index Terms

TSSuBERT: How to Sum Up Multiple Years of Reading in a Few Tweets
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Summarization

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 10 April 2023

Online AM: 21 January 2023

Accepted: 16 January 2023

Revised: 15 November 2022

Received: 08 February 2022

Published in TOIS Volume 41, Issue 4

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Cited By

Ehnert PSchröter J(2024)Key point generation as an instrument for generating core statements of a political debate on TwitterFrontiers in Artificial Intelligence10.3389/frai.2024.12009497Online publication date: 20-Mar-2024
https://doi.org/10.3389/frai.2024.1200949
Paul DRana SSaha SMathew J(2024)Online Summarization of Microblog Data: An Aid in Handling Disaster SituationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334752011:3(4029-4039)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3347520
Garg PChakraborty RDandapat S(2024)OntoDSumm: Ontology-Based Tweet Summarization for Disaster EventsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326602511:2(2724-2739)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266025
Bansal SGowda KKumar N(2024)Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121188236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121188
Garg PChakraborty RGupta SDandapat S(2024)IKDSumm: Incorporating key-phrases into BERT for extractive disaster tweet summarizationComputer Speech & Language10.1016/j.csl.2024.10164987(101649)Online publication date: Aug-2024
https://doi.org/10.1016/j.csl.2024.101649
Garg PChakraborty RDandapat S(2024)ADSumm: annotated ground-truth summary datasets for disaster tweet summarizationSocial Network Analysis and Mining10.1007/s13278-024-01323-914:1Online publication date: 5-Aug-2024
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Dusart APinel-Sauvagnat KHubert G(2023)Exploring unsupervised textual representations generated by neural language models in the context of automatic tweet stream summarizationOnline Social Networks and Media10.1016/j.osnem.2023.10027237-38(100272)Online publication date: Sep-2023
https://doi.org/10.1016/j.osnem.2023.100272

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