Paper: Co-Feedback Ranking for Query-Focused Summarization

ACL ID P09-2030
Title Co-Feedback Ranking for Query-Focused Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors
  • Furu Wei (Hong Kong Polytechnic University, Hung Hom Hong Kong; Wuhan University, Wuhan China; IBM China, Beijing China)
  • Wenjie Li (Hong Kong Polytechnic University, Hung Hom Hong Kong)
  • Yanxiang He (Wuhan University, Wuhan China)

In this paper, we propose a novel ranking framework – Co-Feedback Ranking (Co- FRank), which allows two base rankers to supervise each other during the ranking process by providing their own ranking results as feedback to the other parties so as to boost the ranking performance. The 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 promising.