Essential Pages

Swaminathan, Ashwin; Mathew, Cherian V.; Kirovski, Darko

doi:10.1109/wi-iat.2009.33

Cited by 33 publications

(37 citation statements)

References 9 publications

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“…-KeySRC [3]: a state-of-the-art Web clustering engine built on top of STC with partof-speech pruning and dynamic selection of the cut-off level of the clustering dendrogram. -Essential Pages (EP) [38]: a recent diversification algorithm that selects fundamental pages which maximize the amount of information covered for a given query. -Yahoo!…”

Section: Methodsmentioning

confidence: 99%

“…Other techniques include the use of conditional probabilities to determine which document is most different from higher-ranking ones [9] or use affinity ranking [46], based on topic variance and coverage. More recently, an algorithm called Essential Pages [38] has been proposed to reduce information redundancy and return Web pages that maximize coverage with respect to the input query.…”

Section: Related Workmentioning

confidence: 99%

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Clustering Web Search Results with Maximum Spanning Trees

Marco

Navigli

2011

AI*IA 2011: Artificial Intelligence Around Man and Beyond

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Abstract. We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clustering Web Search Results with Maximum Spanning Trees

Marco

Navigli

2011

AI*IA 2011: Artificial Intelligence Around Man and Beyond

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“…All approaches are formulated as maximum coverage problems, which have been found to provide elegant and effective methods for conventional summarization and diversified retrieval problems [10,28,21]. We start by reviewing the coverage-based summarization idea in the remainder of this section, and then extend it to corpus summarization in Section 4.…”

Section: Summarization As Coveragementioning

confidence: 99%

“…For conventional summarization and diversified retrieval, coverage approaches [21,28,16,23,19] and, more generally, submodular summarization methods [10] represent the state of the art. In particular, they provide an elegant model of the relevance/redundancy trade-off inherent in all summarization problems.…”

Section: Introductionmentioning

confidence: 99%

Temporal corpus summarization using submodular word coverage

Sipoš

Swaminathan

Shivaswamy

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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In many areas of life, we now have almost complete electronic archives reaching back for well over two decades. This includes, for example, the body of research papers in computer science, all news articles written in the US, and most people's personal email. However, we have only rather limited methods for analyzing and understanding these collections. While keyword-based retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a temporal summary of such corpora in terms of landmark documents, authors, and topics. In particular, we explicitly model the temporal nature of influence between documents and re-interpret summarization as a coverage problem over words anchored in time. The resulting models provide monotone sub-modular objectives for computing informative and non-redundant summaries over time, which can be efficiently optimized with greedy algorithms. Our empirical study shows the effectiveness of our approach over several baselines.

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“…Much prior work on this problem has focused on manually-tuned methods for generating diverse results [2][3][4][5][6]. Some learning approaches exist as well and have been shown to outperform manually tuned methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Learning Socially Optimal Information Systems from Egoistic Users

Raman

Joachims

2013

Advanced Information Systems Engineering

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Abstract. Many information systems aim to present results that maximize the collective satisfaction of the user population. The product search of an online store, for example, needs to present an appropriately diverse set of products to best satisfy the different tastes and needs of its user population. To address this problem, we propose two algorithms that can exploit observable user actions (e.g. clicks) to learn how to compose diverse sets (and rankings) that optimize expected utility over a distribution of utility functions. A key challenge is that individual users evaluate and act according to their own utility function, but that the system aims to optimize collective satisfaction. We characterize the behavior of our algorithms by providing upper bounds on the social regret for a class of submodular utility functions in the coactive learning model. Furthermore, we empirically demonstrate the efficacy and robustness of the proposed algorithms for the problem of search result diversification.

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Essential Pages

Cited by 33 publications

References 9 publications

Clustering Web Search Results with Maximum Spanning Trees

Clustering Web Search Results with Maximum Spanning Trees

Temporal corpus summarization using submodular word coverage

Learning Socially Optimal Information Systems from Egoistic Users

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