Passage level evidence for effective document level retrieval

Several researchers have considered the use of passages within documents as useful units of representation as individual passages may capture accurately the topic of discourse in a document. In this work, each document is indexed as a series of unique passages. We explore and analyse a number of similarity measures which take into account the similarity at passage level with the aim of improving the quality of the answer set. We define a number of such passage level approaches and compare their performance. Mean average precision (MAP) and precision at k documents (P@k) are used as measures of the quality of the approaches. The results show that for the different test collections, the rank of a passage is a useful measure, and when used separately or in conjunction with the document score can give better results as compared to other passage or document level similarity approaches.