Estimating Importance Features for Fact Mining (With a Case Study in Biography Mining)

We present a transparent model for ranking sentences that incorporates topic relevance as well as an aboutness and importance feature. We describe and compare five methods for estimating the importance feature. The two key features that we use are graph-based ranking and ranking based on reference corpora of sentences known to be important. Independently those features do not improve over the baseline, but combined they do. While our experimental evaluation focuses on informational queries about people, our importance estimation methods are completely general and can be applied to any topic.