Co-Feedback Ranking for query-focused summarization

Abstract

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. © 2009 ACL and AFNLP.