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Automatic summary assessment is useful for both machine-generated andhuman-produced summaries. Automatically evaluating the summary text given thedocument enables, for example, summary generation system development anddetection of inappropriate summaries. Summary assessment can be run in a numberof modes: ranking summary generation systems; ranking summaries of a particulardocument; and estimating the quality of a document-summary pair on an absolutescale. Existing datasets with annotation for summary assessment are usuallybased on news summarization datasets such as CNN/DailyMail or XSum. In thiswork, we describe a new dataset, the podcast summary assessment corpus, acollection of podcast summaries that were evaluated by human experts atTREC2020. Compared to existing summary assessment data, this dataset has twounique aspects: (i) long-input, speech podcast based, documents; and (ii) anopportunity to detect inappropriate reference summaries in podcast corpus.First, we examine existing assessment methods, including model-free andmodel-based methods, and provide benchmark results for this long-input summaryassessment dataset. Second, with the aim of filtering referencesummary-document pairings for training, we apply summary assessment for dataselection. The experimental results on these two aspects provide interestinginsights on the summary assessment and generation tasks. The podcast summaryassessment data is available.

Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods