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Fact-based Content Weighting for Evaluating Abstractive Summarisation

Xinnuo Xu, Ondřej Dušek, Jingyi Li, Verena Rieser, Ioannis Konstas

202021 citationsDOIOpen Access PDF

Abstract

ive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary will reflect all relevant facts, i.e. the ones present in the ground truth (human-generated refer- ence summary). We confirm this hypothe- sis by showing that our weightings are highly correlated to human perception and compare favourably to the recent manual highlight- based metric of Hardy et al. (2019).

Topics & Concepts

WeightingComputer scienceMetric (unit)Information retrievalNatural language processingWord (group theory)Ground truthContent (measure theory)Artificial intelligenceMachine learningMathematicsMathematical analysisOperations managementGeometryEconomicsMedicineRadiologyTopic ModelingNatural Language Processing TechniquesAdvanced Text Analysis Techniques