Abstract
We address the problem of unsupervised ensemble ranking in this paper. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, we propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to "teach" each other before combination during the ranking process by providing their own ranking results as feedback to the others so as to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. We apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 data set. The results are encouraging with consistent and promising improvements.
Original language | English |
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Title of host publication | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 |
Pages | 1557-1560 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2009 |
Event | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, Hong Kong Duration: 2 Nov 2009 → 6 Nov 2009 |
Conference
Conference | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 2/11/09 → 6/11/09 |
Keywords
- Interactive ranking
- Query-focused summarization
- Rank-learn-combine
- Sentence ranking
- Unsupervised ensemble ranking
ASJC Scopus subject areas
- Decision Sciences(all)
- Business, Management and Accounting(all)