  Despite recent improvements in abstractive summarization, most current
approaches generate summaries that are not factually consistent with the source
document, severely restricting their trust and usage in real-world
applications. Recent works have shown promising improvements in factuality
error identification using text or dependency arc entailments; however, they do
not consider the entire semantic graph simultaneously. To this end, we propose
FactGraph, a method that decomposes the document and the summary into
structured meaning representations (MR), which are more suitable for factuality
evaluation. MRs describe core semantic concepts and their relations,
aggregating the main content in both document and summary in a canonical form,
and reducing data sparsity. FactGraph encodes such graphs using a graph encoder
augmented with structure-aware adapters to capture interactions among the
concepts based on the graph connectivity, along with text representations using
an adapter-based text encoder. Experiments on different benchmarks for
evaluating factuality show that FactGraph outperforms previous approaches by up
to 15%. Furthermore, FactGraph improves performance on identifying content
verifiability errors and better captures subsentence-level factual
inconsistencies.

Computation and Language;Artificial Intelligence;Machine Learning