# A Passage-Based Approach to Learning to Rank Documents

## TL;DR

This paper introduces a novel passage-based learning-to-rank method for document retrieval that leverages passage rankings and features to improve effectiveness over traditional approaches.

## Contribution

It develops new learning-to-rank algorithms that incorporate passage rankings and features, enhancing document retrieval performance.

## Key findings

- Our methods outperform strong baseline models.
- Passage ranking significantly improves document relevance prediction.
- Feature-based passage representations boost retrieval accuracy.

## Abstract

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.