Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Keyword extraction from a single document using centrality measures
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Experiments in Microblog Summarization
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Comparative study of clustering techniques for short text documents
Proceedings of the 20th international conference companion on World wide web
| Hi-index | 0.00 |
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.