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Academic literature does not give much guidance on how to build the bestpossible customer-facing summarization system from existing researchcomponents. Here we present analyses to inform the selection of a systembackbone from popular models; we find that in both automatic and humanevaluation, BART performs better than PEGASUS and T5. We also find that whenapplied cross-domain, summarizers exhibit considerably worse performance. Atthe same time, a system fine-tuned on heterogeneous domains performs well onall domains and will be most suitable for a broad-domain summarizer. Our workhighlights the need for heterogeneous domain summarization benchmarks. We findconsiderable variation in system output that can be captured only with humanevaluation and are thus unlikely to be reflected in standard leaderboards withonly automatic evaluation.

Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness