Mitigating Intrinsic Named Entity-Related Hallucinations of Abstractive Text Summarization (2023)

Abstract: Abstractive text summarization (ATS) is both important and challenging. Recent studies have shown that ATS still faces various forms of hallucination. Our study also indicates that a significant portion of hallucinations is named entity-related. They might appear in different forms, such as mistaken entities and erroneous entity references. The underlying causes implicit in data are complex: data samples pose varying learning conditions. Despite recent research efforts dedicated to named entity-related hallucinations, the solutions have not adequately addressed the varying learning conditions posed by data. This paper aims to bridge the gap in pursuit of reducing intrinsic named entity-related hallucinations. To do so, we propose an adaptive margin ranking loss to facilitate two entity-alignment learning methods to tackle them. Our experiment results show that our methods improve the used baseline model on automatic evaluation scores. The human evaluation also indicates that our methods jointly reduce the intrinsic named entity-related hallucinations considerably compared to the used baseline model.

## introduction abstractive text summarization (ats) has improved considerably in recent years, attributed to the advancement of encoder-decoder (a.k.a. seq2seq) modeling (e.g., @xcite . the language modeling using innovative pre-training methods (e.g., @xcite has further led to the significant performance gain on n-gram overlap-based metrics (e.g. rouges). but recent studies (e.g., @xcite have found generative summaries prone to various forms of factual problems known as hallucinations. despite the research efforts leading to hallucination reduction and factuality improvement (e.g., @xcite @xcite , some hallucinations remain a challenge to ats. named entity-related hallucinations (nerhs) are among them. our study has also found that summarization of long text sequences (e.g., cnn dailymail or cnndm in short) incurs high occurrences of nerhs compared to other types of factual problems (e.g., negation related). nerhs can be divided into intrinsic and extrinsic. the former includes the cases where a named entity hallucinated in a summary is mentioned in the source document, while the latter occurs when a summary includes a novel named entity not in the source document. we are interested in the intrinsic nerhs as the main source of nerhs. among the intrinsic nerhs, entity-entity hallucinations are often observed. for example, given the source document from the cnndm test set below, "... since civil war ..., 310,000 people have been killed, :: for ::::::: human :::::: rights said thursday. ... estimate by the u.n. of at least 220,000 dead. ...". a pre-trained model fine-tuned with the cnndm training data may generate the following summary, "u.n.: more than 310,000 people have been killed in syria ...". the summary mistakes ::: the :::::: syrian :::::::::::: observatory ::: for :::::: human ::::::: rights for u.n. we believe that the cause is rooted in the model's misaligning of entities with their contexts in terms of sentences. the other form of intrinsic nerhs is entityreference hallucinations. for example, given the following source document, "... with an eight-month-old baby, ... savannah guthrie ... help ::: the fine-tuned model generates the summary, "the ... mother-of-one discussed ... tips for getting his eight-month-old daughter to sleep ...". as seen, "her" is mistaken for "his" in the summary. we think that the cause could lie in the reference similarity in the latent space of the model. researchers have proposed entity-aware approaches to tackle intrinsic nerhs (e.g., @xcite . meanwhile, the illustrated examples suggest that the models could fail to learn from data to distinguish positive candidates (e.g., her) from negative ones (e.g., his) in the latent space. margin ranking loss is well-known for tackling such problems. for example, @xcite incorporate a margin ranking loss to address extrinsic entity hallucinations. the classical margin ranking loss treats all samples identically, but various forms of intrinsic nerhs are implicit in data with complicated linguistic structures and might interplay at both lexical and contextual levels. data samples thus pose varying learning conditions (easy or difficult) for a model. and yet it is difficult to predefine and categorize them in modeling. it is desirable for methods adaptive to the variant learning conditions at a fine granularity. perhaps missing such adaptive capacity will have limited the efficacy of the prior works. to bridge the gap in tackling our hypothesized causes of the illustrated examples, we develop an adaptive margin ranking loss to facilitate two learning methods to jointly mitigate the intrinsic nerhs. in summary, our contributions are: ## related work factual issues or hallucinations are challenging to ats despite recent progress. many methods have been proposed in recent years to tackle the challenges. typically, contrastive learning methods are adopted to address the factual issues caused by exposure bias or sample imbalance (e.g., @xcite . the recent progress in summarization evaluation research also inspires new learning objectives (e.g., @xcite and questionanswer-based approaches (e.g., @xcite . additionally, hallucinations in generated summaries prompt post-editing correction methods such as @xcite and @xcite . meanwhile, realizing entity-related hallucinations as one of the main factual issues, researchers have also developed entity-aware methods. one idea is to encode entity-aware representations for expressiveness (e.g., @xcite . to improve named entity matches between the generated summaries and the source documents, @xcite propose a named entity span copy from source documents based on generative likelihood estimation with a global relevance classification task. nan et al. (2021a) introduce a summary-worthy entity classification on the entities occurring in both the source documents and reference summaries. to address named entityrelation hallucinations, lyu et al. (2022) introduce entity-relation generators with entity-consistency and relation-consistency learning objectives. observing the source-summary entity aggregation phenomenon in which the named entities are replaced by more general scoped descriptions, gonzález entity context representation embedding layer bart encoder embedding layer bart decoder entity context representation adaptive margin ranking loss module adaptive margin ranking loss module (a) extended bart encoder-decoder. negative sampler scoring function adaptive margin ranking loss scoring function mlp context-guided entity span classification espan-diou mlp entity & positive sample pairs entity context representation entity span ground truth margin scaling variable function (b) adaptive margin ranking loss module. figure 1: (a) architecture of a bart encoder-decoder (blue-ish block) extended with an entity-sentence alignment method (green-ish block) and an entity-reference alignment method (carmine block). the two alignment methods are internally based on an adaptive margin ranking loss module (milky block) and an entity context representation module (purple-ish block). (b) adaptive margin ranking loss module consists of two key submodules, the margin ranking loss with adaptive capacity (pinkish block) and the margin scaling variable submodule (sky-blue block). mlps are used to reduce dimensionality. et al. (2022) introduce aggregation-awareness to ensure the aggregations are factually aligned with the aggregated entities. introducing guiding signals, zhang et al. (2022) propose an entity coverage control method by prepending an entity coverage precision value to the encoder's inputs while narayan et al. (2021) chain named entities from the reference summary and prepend them to the decoding inputs. post-editing approaches (e.g., chen @xcite are also developed to reduce entity hallucination as part of strategies for improving broader factual consistency. methods discussed so far have not adequately considered adaptiveness to the variant learning conditions at a fine granularity. in developing our adaptive methods, we desire a real-valued marginscaling variable function to provide the resulting margins with three properties. that is, they are proportional to the (easy or difficult) learning conditions implicit in samples; they are real values within a well-defined scalar space permissible for our learning purpose; and the base margin is preserved at the low bound of the scalar space. ## our methods our methods 1 are illustrated in fig. 1 . the architecture as shown in fig. 1a , consists of the backbone bart encoder-decoder @xcite , the 1 as our methods focus solely on named entities, we simply mean "named entity" whenever we use "entity" from now on. entity-sentence alignment method (e-sent am), and the entity-reference alignment method (e-ref am). both alignment methods utilize adaptive margin ranking loss that has the same modular structure as shown in fig. 1b except that the e-sent am uses a similarity-scoring function while the e-ref am adopts an antisymmetric-scoring function. two methods also share an entity context representation module. we first discuss the generic aspects of the architecture before proceeding to the specifics of our two alignment methods. ## conclusion this paper proposes an adaptive margin ranking loss function. we utilize the loss function to facilitate two entity alignment methods to mitigate intrinsic named entity-related hallucinations of ats. our experiment results and analysis show that our methods improve the adopted baseline model on automatic evaluation scores and reduce the intrinsic named entity-related hallucinations.