Ranking Documents by Answer-Passage Quality

Evidence derived from passages that closely represent likely answers to a posed query can be useful input to the ranking process. Based on a novel use of Community Question Answering data, we present an approach for the creation of such passages. A general framework for extracting answer passages and estimating their quality is proposed, and this evidence is integrated into ranking models. Our experiments on two web collections show that such quality estimates from answer passages provide a strong indication of document relevance and compare favorably to previous passage-based methods. Combining such evidence can significantly improve over a set of state-of-the-art ranking models, including Quality-Biased Ranking, External Expansion, and a combination of both. A final ranking model that incorporates all quality estimates achieves further improvements on both collections.