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Despite recent improvements in abstractive summarization, most currentapproaches generate summaries that are not factually consistent with the sourcedocument, severely restricting their trust and usage in real-worldapplications. Recent works have shown promising improvements in factualityerror identification using text or dependency arc entailments; however, they donot consider the entire semantic graph simultaneously. To this end, we proposeFactGraph, a method that decomposes the document and the summary intostructured meaning representations (MR), which are more suitable for factualityevaluation. 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 encoderaugmented with structure-aware adapters to capture interactions among theconcepts based on the graph connectivity, along with text representations usingan adapter-based text encoder. Experiments on different benchmarks forevaluating factuality show that FactGraph outperforms previous approaches by upto 15%. Furthermore, FactGraph improves performance on identifying contentverifiability errors and better captures subsentence-level factualinconsistencies.

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations