The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Video summarization and retrieval using singular value decomposition
Multimedia Systems
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
From single to multi-document summarization: a prototype system and its evaluation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Web-page summarization using clickthrough data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Unsupervised modeling of Twitter conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Summarizing microblogs automatically
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Topical semantics of twitter links
Proceedings of the fourth ACM international conference on Web search and data mining
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to Rank for Query-Focused Multi-document Summarization
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Timeline generation through evolutionary trans-temporal summarization
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Multimedia summarization for trending topics in microblogs
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Twitter has become one of the most popular platforms for users to share information in real time. However, as an individual tweet is short and lacks sufficient contextual information, users cannot effectively understand or consume information on Twitter, which can either make users less engaged or even detached from using Twitter. In order to provide informative context to a Twitter user, we propose the task of Twitter context summarization, which generates a succinct summary from a large but noisy Twitter context tree. Traditional summarization techniques only consider text information, which is insufficient for Twitter context summarization task, since text information on Twitter is very sparse. Given that there are rich user interactions in Twitter, we thus study how to improve summarization methods by leveraging such signals. In particular, we study how user influence models, which project user interaction information onto a Twitter context tree, can help Twitter context summarization within a supervised learning framework. To evaluate our methods, we construct a data set by asking human editors to manually select the most informative tweets as a summary. Our experimental results based on this editorial data set show that Twitter context summarization is a promising research topic and pairwise user influence signals can significantly improve the task performance.