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Finding Diverse, High-Value Representatives on a Surface of Answers

Summary: Proposes k-DHR to extract a diverse, high-value subset from a surface of answers, where elevation encodes quality over answer attributes. Establishes submodular, monotone objective; provides efficient algorithms with guarantees; demonstrates gains on lead-finding and fact-checking tasks, and practical applications. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11573
Venue
VLDB
Year
2017
Pagerank
4.4369118e-05
Overall Rank
8,848 | 38.45%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,686 On Detecting Cherry-picked Trendlines 2020 VLDB 6.84423e-05
7,029 Computational Fact Checking: A Content Management Perspective 2018 VLDB 4.8563777e-05
11,748 Durable Top-k Queries on Temporal Data 2018 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,445 Diversifying Top-K Results 2012 VLDB 0.00011945231
3,340 Toward Computational Fact-Checking 2014 VLDB 7.2030091e-05
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