AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
Tiezheng Yu, Zihan Liu, Pascale Fung
Abstract
State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. In this paper, we present a study of domain adaptation for the abstractive summarization task across six diverse target domains in a low-resource setting.
Topics & Concepts
Automatic summarizationComputer scienceTask (project management)ForgettingAdaptation (eye)Domain adaptationDomain (mathematical analysis)Resource (disambiguation)Artificial intelligenceGeneralizationSimilarity (geometry)Generative grammarMachine learningNatural language processingCognitive psychologyMathematical analysisEconomicsOpticsPhysicsPsychologyManagementClassifier (UML)MathematicsComputer networkImage (mathematics)Topic ModelingNatural Language Processing TechniquesAdvanced Text Analysis Techniques