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arXiv · 2102.09130

Entity-level Factual Consistency of Abstractive Text Summarization

Feng Nan·Ramesh Nallapati·Zhiguo Wang·Cicero Nogueira dos Santos·Henghui Zhu·Dejiao Zhang·Kathleen McKeown·Bing Xiang
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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.

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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang. 2021-02-18. Entity-level Factual Consistency of Abstractive Text Summarization. https://arxiv.org/abs/2102.09130

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