Re-ranking search results using document-passage graphs

Michael Bendersky, Oren Kurland

Abstract: We present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the query similarity of its constituent passages, our approach leverages information about the centrality of the document passages with respect to the initial document list. Passage centrality is induced over a bipartite document-passage graph, wherein edge weights represent document-passage similarities. Empirical evaluation shows that our approach yields effective re-ranking performance. Furthermore, the performance is superior to that of previously proposed passage-based document ranking methods.