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Entity-level Factual Consistency of Abstractive Text Summarization
- Publication Year :
- 2021
-
Abstract
- A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.<br />Comment: EACL 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2102.09130
- Document Type :
- Working Paper