Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2012

Abstract

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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM 2012)

First Page

1173

Last Page

1182

ISBN

9781450311564

Identifier

10.1145/2396761.2398417

Publisher

ACM Press

City or Country

Maui, Hawaii, USA

Additional URL

https://doi.org/10.1145/2396761.2398417

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