  It is well known that the standard likelihood training and approximate
decoding objectives in neural text generation models lead to less human-like
responses for open-ended tasks such as language modeling and story generation.
In this paper we have analyzed limitations of these models for abstractive
document summarization and found that these models are highly prone to
hallucinate content that is unfaithful to the input document. We conducted a
large scale human evaluation of several neural abstractive summarization
systems to better understand the types of hallucinations they produce. Our
human annotators found substantial amounts of hallucinated content in all model
generated summaries. However, our analysis does show that pretrained models are
better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in
generating faithful and factual summaries as evaluated by humans. Furthermore,
we show that textual entailment measures better correlate with faithfulness
than standard metrics, potentially leading the way to automatic evaluation
metrics as well as training and decoding criteria.
