Planning with Learned Entity Prompts for Abstractive Summarization

Citations Over TimeTop 10% of 2021 papers

Abstract

Abstract We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries with entity chains—ordered sequences of entities mentioned in the summary. Transformer-based sequence-to-sequence models are then trained to generate the entity chain and then continue generating the summary conditioned on the entity chain and the input. We experimented with both pretraining and finetuning with this content planning objective. When evaluated on CNN/DailyMail, XSum, SAMSum, and BillSum, we demonstrate empirically that the grounded generation with the planning objective improves entity specificity and planning in summaries for all datasets, and achieves state-of-the-art performance on XSum and SAMSum in terms of rouge. Moreover, we demonstrate empirically that planning with entity chains provides a mechanism to control hallucinations in abstractive summaries. By prompting the decoder with a modified content plan that drops hallucinated entities, we outperform state-of-the-art approaches for faithfulness when evaluated automatically and by humans.

Related Papers

• → Few-Shot Learning via Saliency-Guided Hallucination of Samples(2019)235 cited
• → Source monitoring deficits in hallucinating compared to non-hallucinating patients with schizophrenia(2006)76 cited
• → Learning to Hallucinate Examples from Extrinsic and Intrinsic Supervision(2021)5 cited
• → Hallucinations in Children: A Follow‐up Study(1988)39 cited
• The hallucinating patient and nursing intervention.(1976)