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Entity-level Factual Consistency of Abstractive Text Summarization

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arxiv 2102.09130 v1 pith:PCFIT7NM submitted 2021-02-18 cs.CL cs.AI

Feng Nan , Ramesh Nallapati , Zhiguo Wang , Cicero Nogueira dos Santos , Henghui Zhu , Dejiao Zhang , Kathleen McKeown , Bing Xiang This is my paper
classification cs.CLcs.AI
keywords entityconsistencyfactualabstractivedocumententity-levelgeneratedhallucination
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation

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    The paper proposes TG-DRG and GA-DRG, two graph-augmented frameworks that combine coreference resolution, knowledge selection, and graph encoding to improve factuality of dialogue responses, evaluated with a newly pro...

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