FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
David Wan, Mohit Bansal
2022Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies53 citationsDOIOpen Access PDF
Abstract
We present FACTPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and finetuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 2020) pre-training objective to create pseudosummaries that are both important and factual;
Topics & Concepts
Automatic summarizationComputer scienceSentenceArtificial intelligenceNatural language processingShot (pellet)Selection (genetic algorithm)Transfer of learningFine-tuningQuantum mechanicsOrganic chemistryPhysicsChemistryTopic ModelingNatural Language Processing TechniquesAdvanced Text Analysis Techniques