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A tweet summarization method based on a keyword graph

Published: 09 January 2014 Publication History

Abstract

There are a huge number of posts on the micro blogs such as Twitter and thus it can be an important information source of various domains. However, the information density of each post, tweet, is too low because the length of tweets is too short. Due to the huge amount and low information density, it is hard to obtain useful information from Twitter such as the public opinion trend. Considering these characteristics of tweets, we propose a novel tweet summarization method. The proposed method first finds the strongly related groups of words based on keyword graphs. In the graphs, the frequent words are the vertexes and the co-occurrences are the edges. We use the maximum k-clique method to find strongly related groups of words, and summarize the tweets which include the words in groups. We confirmed the proposed method is effective for summarizing of tweets and is superior to the existing method with the experiments.

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cover image ACM Conferences
ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
January 2014
757 pages
ISBN:9781450326445
DOI:10.1145/2557977
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 January 2014

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Author Tags

  1. K-clique
  2. Twitter
  3. co-occurring graph
  4. tweet summarization

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ICUIMC '14 Paper Acceptance Rate 116 of 407 submissions, 29%;
Overall Acceptance Rate 251 of 941 submissions, 27%

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  • (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
  • (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
  • (2022)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research QuestionsAnalytics10.3390/analytics10200071:2(72-97)Online publication date: 23-Sep-2022
  • (2019)Tweet Summarization of News Articles: An Objective Ordering-Based PerspectiveIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29261446:4(761-777)Online publication date: Aug-2019
  • (2019)Event modeling and mining: a long journey toward explainable eventsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00545-029:1(459-482)Online publication date: 1-Jul-2019
  • (2018)Tweet Summarization: A New Approch2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)10.1109/ICICCT.2018.8473327(1022-1025)Online publication date: Apr-2018
  • (2015)Multi-genre summarization: Approach, potentials and challengeseChallenges e-2015 Conference10.1109/eCHALLENGES.2015.7440970(1-9)Online publication date: Nov-2015
  • (2015)K-medoids algorithm on Indonesian Twitter feeds for clustering trending issue as important terms in news summarization2015 International Conference on Information & Communication Technology and Systems (ICTS)10.1109/ICTS.2015.7379878(95-98)Online publication date: Sep-2015

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