  Abstractive text summarization has garnered increased interest as of late, in
part due to the proliferation of large language models (LLMs). One of the most
pressing problems related to generation of abstractive summaries is the need to
reduce "hallucinations," information that was not included in the document
being summarized, and which may be wholly incorrect. Due to this need, a wide
array of metrics estimating consistency with the text being summarized have
been proposed. We examine in particular a suite of unsupervised metrics for
summary consistency, and measure their correlations with each other and with
human evaluation scores in the wiki_bio_gpt3_hallucination dataset. We then
compare these evaluations to models made from a simple linear ensemble of these
metrics. We find that LLM-based methods outperform other unsupervised metrics
for hallucination detection. We also find that ensemble methods can improve
these scores even further, provided that the metrics in the ensemble have
sufficiently similar and uncorrelated error rates. Finally, we present an
ensemble method for LLM-based evaluations that we show improves over this
previous SOTA.
