  Neural abstractive summarization models are prone to generate summaries which
are factually inconsistent with their source documents. Previous work has
introduced the task of recognizing such factual inconsistency as a downstream
application of natural language inference (NLI). However, state-of-the-art NLI
models perform poorly in this context due to their inability to generalize to
the target task. In this work, we show that NLI models can be effective for
this task when the training data is augmented with high-quality task-oriented
examples. We introduce Falsesum, a data generation pipeline leveraging a
controllable text generation model to perturb human-annotated summaries,
introducing varying types of factual inconsistencies. Unlike previously
introduced document-level NLI datasets, our generated dataset contains examples
that are diverse and inconsistent yet plausible. We show that models trained on
a Falsesum-augmented NLI dataset improve the state-of-the-art performance
across four benchmarks for detecting factual inconsistency in summarization.
  The code to obtain the dataset is available online at
https://github.com/joshbambrick/Falsesum
