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
Proceedings of the 11th international conference on World Wide Web
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
DivRank: the interplay of prestige and diversity in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method which works in an iterative fashion based on random walks to achieve diversity. Specifically, we use negative reinforcement as a main tool to introduce diversity in the Personalized PageRank framework. Experiments on two benchmark datasets show that our algorithm is competitive to the existing methods.