Mitigating Hallucination in Abstractive Summarization with
  Domain-Conditional Mutual Information
Kyubyung Chae Jaepill Choi Yohan Jo Taesup Kim
A primary challenge in abstractive summarization is hallucination -- the
phenomenon where a model generates plausible text that is absent in the source
text. We hypothesize that the domain (or topic) of the source text triggers the
model to generate text that is highly probable in the domain, neglecting the
details of the source text. To alleviate this model bias, we introduce a
decoding strategy based on domain-conditional pointwise mutual information.
This strategy adjusts the generation probability of each token by comparing it
with the token's marginal probability within the domain of the source text.
According to evaluation on the XSUM dataset, our method demonstrates
improvement in terms of faithfulness and source relevance. The code is publicly
available at \url{https://github.com/qqplot/dcpmi}.