Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization (2022)

Abstract: Existing abstractive summarization systems are hampered by content hallucinations in which models generate text that is not directly inferable from the source alone. Annotations from prior work have shown that some of these hallucinations, while being ‘unfaithful’ to the source, are nonetheless factual. Our analysis in this paper suggests that these factual hallucinations occur as a result of the prevalence of factual yet unfaithful entities in summarization datasets. We find that these entities are not aberrations, but instead examples of additional world knowledge being readily used to latently connect entities and concepts – in this case connecting entities in the source document to those in the target summary. In our analysis and experiments, we demonstrate that connecting entities to an external knowledge base can lend provenance to many of these unfaithful yet factual entities, and further, this knowledge can be used to improve the factuality of summaries without simply making them more extractive.

## introduction despite producing fluent summaries with excellent automatic evaluation scores, current abstractive summarization methods routinely hallucinateproducing content that is not directly supported by the source text @xcite . for instance, @xcite demonstrated that 92% of the summaries generated by various summarization models on xsum @xcite contain at least one factual error. in addition, the majority of these hallucinated errors are entity-based. @xcite divided these hallucinations into intrinsic and extrinsic. while intrinsic hallucination focuses on errors that are incorrectly aggregated from the source, the majority of hallucinations are extrinsic, meaning they cannot be inferred directly from the source. most prior work considers ## improving factual consistency with world knowledge in this section, we explore whether we can incorporate additional knowledge into a summarization model to reduce content hallucination and produce more faithful summaries. two approaches for incorporating external knowledge into summarization are investigated: (i) directly concatenate the knowledge subgraph to the source for a standard sequence-to-sequence summarization model; (ii) refine initial sequence-to-sequence output with a second-stage entity revision model that has access to relevant facts. it is worth noting that there are a lot of options for the second-stage entity-revision model, and determining the "optimal" revision model will require more research. instead, we aim to show that improving the factuality of generated summaries with a two-stage revision technique is a viable option. ## main results this section discusses the results of using the revision model to correct entity errors. we divide the results into two parts: 1) oracle correction on gold-reference summaries, and 2) revision model based on system-generated summaries. we propose using entity correctness as the criteria for assessing the factuality of generated entities. 5 entity correctness matches predicted entities to the the target entities. surface forms are linked to their wikidata ids to resolve entity matching in account for entity paraphrase (section 2.1). all target entities in the summaries are further divided into two categories: abstractive entities that do not appear in the source and extractive entities that do. the abstractive subset frequently require external knowledge to infer the target entity. ## analysis and discussion faithfulness vs. factuality although factuality and faithfulness are frequently used interchangeably in the literature, in our notion, they measure the generation from completely different perspectives. additionally, they can occasionally have a negative correlation if the targets have a high proportion of abstractive entities that require external knowledge to resolve, like on xsum. this is because extractive entities are encouraged to enhance the faithfulness of the generated summaries. in contrast, abstractive entities that match the targets are frequently encourages by the factuality metric. in section 4, we demonstrated that models using external knowledge may increase the generation's factuality. this section assesses the faithfulness of the generations. we can see from ## related work faithfulness in summarization faithful consistency of summarization has drawn much research interest since the proposal of factcc @xcite , an evaluation model that classifies the generated summary as consistent/inconsistent to the source. later, several question answering-based summarization evaluation methods were proposed @xcite @xcite , in addition to diagnostic datasets @xcite . these models measure the faithfulness by evaluating answers that are produced by a qa model with inputs of (question, source) and (question, generated summary). numerous strategies are also proposed to increase faithfulness by imposing constraint w.r.t the source, such as quantity entity matching @xcite , intermediate planning with entity chains @xcite , extensible guidance @xcite ), document's knowledge graph @xcite , and simple filtering @xcite . in addition, filippova (2020) controls hallucination with unconditional and conditional lms. dong et al. (2020); cao et al. (2020) propose post-error corrections with qa-based models or denoising bart. cao et al. (2018); zhu et al. ( 2021 ) utilize dependency parsing tools to identify and match the relations in an input document to its summary. @xcite propose a novel detection approach that separates factual from non-factual hallucinations. @xcite proposes to prime summarization models with embeddings that are learned through transe on knowledge graphs. additionally, many recent models have been proposed for retrieval augmented language models using passages @xcite , mentions @xcite , and facts @xcite . in this paper, we experiment with incorporating facts that are directly linked to the entities in the source. several models have been proposed to combine symbolically interpretable factual information and subsymbolic neural knowledge @xcite @xcite . different from the previous works, we investigate how facts in external open-domain knowledge can help with entity factuality of the generated summary. ## conclusion in this paper, we show that a large portion of socalled external hallucinations in text summarization can be explained by external knowledge and verified by kb facts connecting source entities to target entities. we have explored multiple ways to combine this knowledge into a faithful and factual generation of summaries. our research proposes a pipeline that, with a solid knowledge base as a foundation, can guarantee better factuality. fur-thermore, we discuss some valuable insights about current limitations and promising directions for knowledge-grounded text generation.