  According to common relevance-judgments regimes, such as TREC's, a document
can be deemed relevant to a query even if it contains a very short passage of
text with pertinent information. This fact has motivated work on passage-based
document retrieval: document ranking methods that induce information from the
document's passages. However, the main source of passage-based information
utilized was passage-query similarities. We address the challenge of utilizing
richer sources of passage-based information to improve document retrieval
effectiveness. Specifically, we devise a suite of learning-to-rank-based
document retrieval methods that utilize an effective ranking of passages
produced in response to the query; the passage ranking is also induced using a
learning-to-rank approach. Some of the methods quantify the ranking of the
passages of a document. Others utilize the feature-based representation of
passages used for learning a passage ranker. Empirical evaluation attests to
the clear merits of our methods with respect to highly effective baselines. Our
best performing method is based on learning a document ranking function using
document-query features and passage-query features of the document's passage
most highly ranked.
