  Hallucinations in text generation occur when the system produces text that is
not grounded in the input. In this work, we tackle the problem of
hallucinations in neural chart summarization. Our analysis shows that the
target side of chart summarization training datasets often contains additional
information, leading to hallucinations. We propose a natural language inference
(NLI) based method to preprocess the training data and show through human
evaluation that our method significantly reduces hallucinations. We also found
that shortening long-distance dependencies in the input sequence and adding
chart-related information like title and legends improves the overall
performance.
