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We carry out a comprehensive evaluation of 13 recent models for ranking oflong documents using two popular collections (MS MARCO documents and Robust04).Our model zoo includes two specialized Transformer models (such as Longformer)that can process long documents without the need to split them. Along the way,we document several difficulties regarding training and comparing such models.Somewhat surprisingly, we find the simple FirstP baseline (truncating documentsto satisfy the input-sequence constraint of a typical Transformer model) to bequite effective. We analyze the distribution of relevant passages (insidedocuments) to explain this phenomenon. We further argue that, despite theirwidespread use, Robust04 and MS MARCO documents are not particularly useful forbenchmarking of long-document models.

Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding