Joint topic modeling for event summarization across news and social media streams

Social media streams such as Twitter are regarded as faster first-hand sources of information generated by massive users. The content diffused through this channel, although noisy, provides important complement and sometimes even a substitute to the traditional news media reporting. In this paper, w...

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Main Authors: GAO, Wei, LI, Peng, DARWISH, Kareem
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/4589
https://ink.library.smu.edu.sg/context/sis_research/article/5592/viewcontent/p1173_gao.pdf
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spelling sg-smu-ink.sis_research-55922019-12-26T07:52:20Z Joint topic modeling for event summarization across news and social media streams GAO, Wei LI, Peng DARWISH, Kareem Social media streams such as Twitter are regarded as faster first-hand sources of information generated by massive users. The content diffused through this channel, although noisy, provides important complement and sometimes even a substitute to the traditional news media reporting. In this paper, we propose a novel unsupervised approach based on topic modeling to summarize trending subjects by jointly discovering the representative and complementary information from news and tweets. Our method captures the content that enriches the subject matter by reinforcing the identification of complementary sentence-tweet pairs. To valuate the complementarity of a pair, we leverage topic modeling formalism by combining a two-dimensional topic-aspect model and a cross-collection approach in the multi-document summarization literature. The final summaries are generated by co-ranking the news sentences and tweets in both sides simultaneously. Experiments give promising results as compared to state-of-the-art baselines. 2012-11-02T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4589 info:doi/10.1145/2396761.2398417 https://ink.library.smu.edu.sg/context/sis_research/article/5592/viewcontent/p1173_gao.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
GAO, Wei
LI, Peng
DARWISH, Kareem
Joint topic modeling for event summarization across news and social media streams
description Social media streams such as Twitter are regarded as faster first-hand sources of information generated by massive users. The content diffused through this channel, although noisy, provides important complement and sometimes even a substitute to the traditional news media reporting. In this paper, we propose a novel unsupervised approach based on topic modeling to summarize trending subjects by jointly discovering the representative and complementary information from news and tweets. Our method captures the content that enriches the subject matter by reinforcing the identification of complementary sentence-tweet pairs. To valuate the complementarity of a pair, we leverage topic modeling formalism by combining a two-dimensional topic-aspect model and a cross-collection approach in the multi-document summarization literature. The final summaries are generated by co-ranking the news sentences and tweets in both sides simultaneously. Experiments give promising results as compared to state-of-the-art baselines.
format text
author GAO, Wei
LI, Peng
DARWISH, Kareem
author_facet GAO, Wei
LI, Peng
DARWISH, Kareem
author_sort GAO, Wei
title Joint topic modeling for event summarization across news and social media streams
title_short Joint topic modeling for event summarization across news and social media streams
title_full Joint topic modeling for event summarization across news and social media streams
title_fullStr Joint topic modeling for event summarization across news and social media streams
title_full_unstemmed Joint topic modeling for event summarization across news and social media streams
title_sort joint topic modeling for event summarization across news and social media streams
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/4589
https://ink.library.smu.edu.sg/context/sis_research/article/5592/viewcontent/p1173_gao.pdf
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