CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Year: 2023

Abstract:
Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on factual errors in the training data. In this work, we propose a new method called Contrastive Parameter Ensembling (CaPE) to use training data more effectively, utilizing variations in noise in training samples to reduce hallucination. Starting with a base model finetuned on an entire dataset, we additionally train expert and anti-expert models on clean and noisy subsets of the data, respectively. We then adjust the parameters of the base model by adding (subtracting) the parameters of the expert (anti-expert), advancing the recent work on additive parameter ensembling approaches. Trained on a much smaller data subset, expert and anti-expert models only fractionally (<14%) increases the total training time. Further, CaPE uses parameter ensembling and does not increase the inference time. Experimental results show that CaPE improves performance across different automatic factual metrics and human evaluation, with a maximum improvement of 16.69% and 15.38% on summary-level dependency-arc entailment accuracy for the XSUM and CNN/DM datasets. The CaPE model performs comparably to the base model on metrics of informativeness such as ROUGE.

heading: 1 Introduction
text: Neural abstractive summarization systems have been shown to generate plausible summaries with high lexical overlap with the references. However, human analyses (Fabbri et al., 2021a; Pagnoni et al., 2021; Tejaswin et al., 2021) and automatic evaluations (Falke et al., 2019; Kryscinski et al., 2020; Maynez et al., 2020; Durmus et al., 2020) show that state-of-the-art models trained on the widely used XSUM (Narayan et al., 2018) and CNN/DM (Hermann et al., 2015) datasets tend to hallucinate
†work was done at Salesforce AI Research.
information with high frequency. The degree of a model’s hallucinations further correlates with the quality of the training data (Aralikatte et al., 2021; Pagnoni et al., 2021). For instance, models trained on the XSum data tend to generate a higher proportion of factual errors as compared to models trained on the CNN/DM dataset.
Given the association between training data quality and hallucinations in resulting models, the easiest method to reduce hallucinations is to remove noisy samples from the training data (Nan et al., 2021). However, data filtering reduces the size of training data and consequently, the diversity in target summary since the removed noisy samples might also include useful task-specific knowledge. This impacts other aspects of the generated summaries such as information recall or fluency. In Table 1, we show ROUGE (R-1/2/L) and named entity recall (E-Rref ) with respect to the reference summary of a BART model (Lewis et al., 2020) trained on the entity precision-filtered XSUM data
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(24.6% of the original data). The new model drops 8-18% in ROUGE and 20% drop in entity recall.
In this work, we design a simple yet effective strategy to utilize both clean and noisy training samples. Based on the observation that the level of hallucination in a summarization model correlates with the level of noise in the training data, we propose Contrastive Parameter Ensembling (CaPE), which adjusts the parameters of a base model by adding (subtracting) the weights of a model trained on clean (noisy) subsets of the data. This approach is motivated by recent work demonstrating the effectiveness of a simpler form of checkpoint averaging in neural machine translation (Sennrich et al., 2016; Vaswani et al., 2017).
CaPE also builds on other recent work on model ensembling. Jacobs et al. (1991); Liu et al. (2021a) combines expert and anti-expert predictions by taking a weighted average of their output. However, this requires running each model separately and increases computational cost linearly in the number of models, further slowing the auto-regressive generation of summaries. Alternatively, Madotto et al. (2020) proposed attention over parameters that jointly optimizes multiple models and directly combines all their parameters through learned attention coefficients. Furthermore, Wortsman et al. (2021) recently showed that the average of a pretrained CLIP (Radford et al., 2021) model with its another version that is further fine-tuned on a new data distribution performs better than both models on their complementary distributions.
We evaluate our CaPE model on two benchmark abstractive summarization datasets, XSUM and CNN/DM. We train an expert and an anti-expert corresponding to each of the dependency-arc entailment (Goyal and Durrett, 2020, 2021) and entity overlap (Nan et al., 2021) metrics. Then, we combine each expert and anti-expert pair to obtain four variants of CaPE and evaluate them using the metrics used for data selection E-Psrc, as well as a different entailment metric, MNLI (Williams et al., 2018), and two question answering-based metrics, QuestEval (Scialom et al., 2021) and QAFactEval (Fabbri et al., 2021b), for factual consistency. We find that all variants of our CaPE consistently outperform the state-of-the-art models on all factual metrics, with marginal variations in ROUGE scores and information recall.

heading: 2 Contrastive Parameter Ensembling
text: In this work, we propose Contrastive Parameter Ensembling (CaPE) for reducing hallucinations in text summarization systems. This method refines a base summarization model by training two additional models, an expert and an anti-expert model. An ensemble model is then constructed through a simple linear combination of the parameters of the three models, an approach inspired by recent work on weight (a.k.a. parameter)-space ensembling (Izmailov et al., 2018; Frankle et al., 2019; Neyshabur et al., 2020; Wortsman et al., 2021).

heading: 2.1 Measuring Hallucinations for Selecting Training Data
text: To select data for training the expert and anti-expert, we assume the availability of automated metrics for measuring hallucinations in reference summaries. There are several automatic metrics to evaluate factual consistency such as entity overlap (Nan et al., 2021), entailment score (Kryscinski et al., 2020; Goyal and Durrett, 2020; Maynez et al., 2020), and QA-based metrics (Durmus et al., 2020; Scialom et al., 2021). These methods vary greatly in computational cost and agreement with human judgments of factuality. We use two of the faster metrics that are based on entity overlap and entailment metrics and have shown good correlations with human evaluations, described below.
Entity Overlap is the simplest method measuring token-level named-entity overlap between the summary and source document (Nan et al., 2021). We use entity token precision (E-Psrc), the percentage of named-entities tokens in the summary that are also present in the source. This metric can be used as a proxy to measure simpler cases of hallucinations, such as out-of-article entity errors (Pagnoni et al., 2021; Zhou et al., 2021; Cao et al., 2022), also known as extrinsic hallucinations (Maynez et al., 2020). A human study by Pagnoni et al. (2021) finds this to be the most frequent form of error in models trained on XSUM data. However, it fails to capture intricate cases of hallucinations such as semantic frame errors (e.g., when an entity is present in the source but is attributed to the wrong predicate).
DAE (Dependency Arc Entailment) (Goyal and Durrett, 2021) measures fine-grained entailment by breaking the summary into smaller claims defined by dependency arcs, covering errors such as incor-
rect predicates or their arguments, coreference errors and discourse link errors, in contrast to the simpler token-level entity overlap. Dependency arcs define grammatical structures in a sentence and often describe semantic connections between words, such as predicate-argument relations. Pagnoni et al. (2021) finds that DAE correlates with human judgment of factuality, and has the highest correlation with complex discourse errors, such as entity coreference. Therefore, we use DAE errors, defined as the number of dependency arcs in the summary that are not entailed by the source document, to identify cases of more intricate hallucinations for selecting training data.

heading: 2.2 Expert and Anti-Expert Parameter Adjustment
text: Using the entity overlap or DAE error metric, we select samples for training expert and anti-expert models that are then used to adjust the base model parameters. The data selection strategy, SELECTCLEAN (SELECTNOISY), and the generic process for building CaPE are described below and further illustrated in Algorithm 1. SELECTCLEAN (SELECTNOISY): For the entity overlap metric, we select clean (noisy) samples with entity precision above (below) a predefined threshold ϵE−Psrcclean (ϵ E−Psrc noisy ). For the DAE error metric, we select clean (noisy) samples with the number of DAE errors below (above) a predefined threshold ϵDAEerrorclean (ϵ DAEerror noisy ). Expert (Anti-Expert) Fine-tuning We train a base summarization model using all training data and then fine-tune this model on the clean (noisy) dataset to obtain the expert (anti-expert). By training on the full data followed by fine-tuning on clean (noisy) subset, we want our expert (anti-expert) model to retain other aspects such as ROUGE and information recall of the base model, and only differ in the factual qualities. As noted in Table 1, this is in contrast to training a BART model on just clean (or noisy) samples that severely deteriorates ROUGE and information recall (analyzed further in § 4.3). Finally, given a mixing coefficient α, we obtain our Contrastive Parameter Ensembled model (θCaPE) from base (θB), expert (θE) and anti-expert (θĒ) parameters following θCaPE = θB+α(θE−θĒ). The mixing coefficient (α) balances factual quality with other aspects of summarization such as ROUGE.
Initializing the expert (anti-expert) from the base
Algorithm 1 CaPE for Summarization Require: Training Data DT , Measure of hallucination MH 1: Train θB on DT 2: Dclean← SELECTCLEAN (DT , MH ) 3: Dnoisy ← SELECTNOISY (DT , MH ) 4: θE ← Fine-tune θB on Dclean 5: θĒ ← Fine-tune θB on Dnoisy 6: θCaPE ← θB + α(θE − θĒ) 7: return θCaPE
or BART model is critical; prior work (Izmailov et al., 2018; Frankle et al., 2019; Neyshabur et al., 2020) has shown that parameter-averaging works well when all constituent models share the same optimization trajectory. On the other hand, averaging parameters of disjointly trained deep neural models, starting from different initializations, may not work better than a model with randomly assigned parameters. Since both methods of fine-tuning and training have a common initialization, the resulting CaPE model exhibits performance comparable to the base model or expert.

heading: 2.3 CaPE: A generalization of WiSE-FT
text: Contrastive Paremeter Ensembling generalizes the recently proposed WiSE-FT (Eq. 1) model (Wortsman et al., 2021), which only performs a weighted sum of a base model and a single fine-tuned model, for ensuring distributional robustness on image classification.
θWiSE−FT = (1.− α)θB + (α)θE (1)
Essentially, θWiSE−FT is a special case of θCaPE where the anti-expert is a base model. We believe Eq. 1 a sub-optimal solution for our objective of minimizing factual errors. Being trained on the noisiest subset of the training data, the anti-expert model hallucinates with higher frequency than the base and expert models, removing parameters responsible for hallucinations more than the other two. We empirically find that our proposed contrastive ensembling outperforms the models that just use one of the expert or anti-expert in § 4.4.

heading: 3 Results
text: We evaluate CaPE on the XSUM (Narayan et al., 2018) and CNN/DM (Hermann et al., 2015) datasets. The XSUM data is highly abstractive and noisy, while CNN/DM is more extractive and contains fewer factual errors (Tejaswin et al., 2021). These data variations allow us to evaluate CaPE under different data quality settings. Besides the
standard ROUGE-1/2/L (R1/R2/RL) scores, we use a diverse set of metrics for evaluating factual consistency and summary quality.
• Darc measures the percentage of dependency arcs in the summary entailed by the source article.
• Dsum measures the percentage of summaries that do not have any dependency arc error.
• E-Psrc measures the percentage of entities in the summary that are present in the source article.
• E-Rref measures the percentage of entities in the reference that are also present in the generated summary.
• BS-P (R) represents the BERTScore precision (recall) w.r.t. the source article (Zhang et al., 2019).
• QEval represents a QA-based factual consistency metric (Scialom et al., 2021).
• MNLI measures the entailment score based on the RoBERTa large (Liu et al., 2019) model trained on MNLI dataset (Williams et al., 2018). The score of a summary sentence is the maximum entailment score over all input sentences, and the final score is averaged across summary sentences as in Laban et al. (2022).
• QAFactEval is another QA-based factual consistency metric that improves question filtering and answer overlap components (Fabbri et al., 2021b).

heading: 3.1 Models
text: We use the BART-based summarization (BARTsum) models released with Huggingface’s transformers library (Wolf et al., 2020) (bart-xsum/cnn-large) as the base models. From human analyses, Pagnoni et al. (2021); Fabbri et al. (2021a) find that BARTsum models generated summaries have the least number of factual errors. We adopt the standard hyperparameters for all models during the inference.
We train an expert (anti-expert) for each of the DAE error (ExpDAE (AntiDAE)) and entity overlap (ExpE−P (AntiE−P )) metrics. We evaluate four variants of CaPE. CaPEPP uses ExpE−P and AntiE−P , CaPEDP uses ExpDAE and AntiE−P (CaPEDD and CaPEPD follow the same naming convention). Depending on the value of α, CaPE may reduce ROUGE or information recall while improving factual consistency. Therefore, for each
variant of CaPE, we select the α such that it does not underperform the base model by more than 1% on ROUGE 1 (R1) and entity recall (E-Rref ) metrics on the validation set1. Baselines: We compare CaPE with two summarization baselines: the base model (BARTsum) and an ensemble of BART-based summarization models. The ensemble model uses the average of a base summarization and two other summarization models obtained by fine-tuning the base model on two randomly sampled subsets of the training data. We also compare CaPE to three post-processing models. The first is a variation of the autoregressive fact correction model from Dong et al. (2020), in which we train a BART-large model to produce the reference summary conditioned on the concatenation of the source and reference summary with all entity slots masked (PP). The second is a modified version of PP trained on the subset of data with an entity precision of 100 (PP-clean). The last is the model from Chen et al. (2021), which generates candidate summaries by (1) enumerating all ways to replace entities in summary with entities of similar type in the input and (2) training BART with an additional classification layer to re-rank these summaries (PP-CC).

heading: 3.2 Automatic Evaluation
text: Table 2 summarizes the results on the XSUM and CNN/DM datasets. First, we find that the ensemble model slightly improves ROUGE scores, BERTScore recall and entity recall on the CNN/DM dataset, but not necessarily factual consistency metrics. On the other hand, all variants of CaPE outperform the base as well as the ensemble across all factual consistency metrics on both the XSUM and CNN/DM datasets. Given the controllability achieved by α, we ensure that all variants of CaPE preserve ROUGE scores and information recall within a pre-defined threshold of maximum 1% drop from the base model. We also find that CaPE models improve BERTScore precision (BS-P) with respect to the source article on both XSUM and CNN/DM. This is noteworthy given recent work on benchmarking different evaluation metrics that suggests that BERTScore precision with respect to the source document correlates with the human judgments of factuality (Pagnoni et al., 2021).
CaPE models also outperform the post-
1We find α using grid search, assigning a minimum value of 0.2 and incrementing it by a step size of 0.2.
processing-based approaches PP and PP-CC on XSUM and all three PP, PP-clean and PP-CC approaches on CNN/DM dataset by a significant margin. However, PP-clean performs similarly to CaPE on factual consistency metrics on XSUM and even obtains a higher E-Psrc score of 72.98. At the same time, PP-clean lowers the performance on ROUGE and information recall, reducing E-Rref performance by ∼15% (underlined in Table 2). Fortunately, we can set the mixing coefficient α in CaPE to a higher value, achieving higher factual consistency at the cost of reduced ROUGE and information recall. To confirm this, we also report the performance of CaPEDP ∗ on XSUM data which uses ExpDAE and AntiE−P mixed with α value of 1.0 (underlined results in Table 2). We find that CaPEDP ∗ obtains much higher scores than the PP-Clean model on all factual consistency metrics, while competently retaining the information recall of the base model (E-Rref reduced by 3.5% com-
pared to ∼15% drop for PP-clean). Finally, in Table 3, we compare CaPEDP (the variant of CaPE with the best trade-off, discussed in §4.2), base and PP-clean models using two additional metrics, QAFactEval and MNLI. As noted by Fabbri et al. (2021b), prior studies comparing factual metrics draw inconsistent conclusions, with a few observing QA-based metrics as superior to entailment metrics (Durmus et al., 2020; Scialom et al., 2021) and others reporting the opposite (Maynez et al., 2020). To the best of our knowledge, QAFactEval performs the best on the SummaC benchmark (Laban et al., 2022), used for comparing factual consistency metrics. On both metrics, we find that CaPEDP outperforms both base and PP-clean models, improving the QAFactEval score by 4.8% and 1.14% over the base model on XSUM and CNN/DM, respectively.
Transferability of Experts (Anti-Experts): We observe that CaPE models also improve performance on the metrics that were not used for training the expert or anti-expert. For instance, CaPEPP outperforms base model on the Darc/Dsum metrics, and CaPEDD outperforms base model on the EPsrc metrics on both XSUM and CNN/DM. All variants of CaPE also outperform base model on QEval, QAFactEval and MNLI, which were also
not used during the development of experts (antiexperts). Secondly, we find that the experts and anti-experts are interchangeable, an expert trained on data selected using one metric can be used in conjunction with an anti-expert based on another metric. As evident, both CaPEDP and CaPEPD outperform base model, with CaPEDP achieving best trade-offs among other variants of CaPE on the XSUM data, discussed further in §4.2. Computational Efficiency: We also report the approximate training (TT) and inference (IT) time for different models relative to the base model in Table 2. We exclude the time required for data processing (e.g. data selection for CaPE and PPClean during training, or entity recognition for all post-processing-based models both during training and inference). We find that CaPE models only marginally increase the training time (≤14%) required for fine-tuning expert (anti-expert) on a smaller selected subset of training data. Further, CaPE models do not increase the inference time. In comparison, post-processing methods use separate models for correcting summaries generated by the base model, increasing the memory required to store the additional model as well as both the training and inference time.

heading: 3.3 Human Evaluation
text: Following Cao and Wang (2021), we also perform a pairwise comparison of summaries, where human annotators rate each CaPEDP -generated summary against the base model-generated summary for factual consistency. We rate 100 random articles from each of the XSUM and CNN/DM datasets. The inter-annotator agreement is 0.8385 (Krippendorff, 2011) based on our sampled articles-summary pairs from XSUM. Annotators find CaPEDP improves (degrades) factual consistency on 19% (14%) summaries on XSUM data and improves (degrades) factual consistency on 6% (2%) summaries on CNN/DM data. Factual consistency remained unchanged for the remaining 67% and 92% summaries from the XSUM and CNN/DM datasets, respectively. We show a few sample outputs illustrating the qualitative effect of α and common errors corrected by the CaPE model in § B.

heading: 4 Analysis

heading: 4.1 Expert (Anti-Expert) Performance
text: In Table 4, we compare the performance of individual expert and anti-expert models on DAE- and
entity-based metrics. We observe: An expert reduces hallucinations in generated summaries. We find that all experts are able to achieve improved performance on the metric used for selecting the training data subset. We further observe that the improvements for experts are not limited to the metrics used for data selection. For instance, ExpDAE improves entity precision (EPsrc) by ∼6% and ExpE−P improves Darc and Dsum by ∼3%. An anti-expert increases hallucinations in generated summaries. All anti-experts reduce performance on factual consistency metrics, with the maximum drop seen on summary-level Dsum metric, indicating that a greater proportion of antiexpert generated summaries are hallucinated. At the same time, they generate well-formed summaries, as indicated by their high (slightly worse than the base model) ROUGE scores. This is the desired behavior for an anti-expert that should generate hallucinated but well-formed summaries.

heading: 4.2 Effects of Mixing Coefficient α
text: We combine the expert and anti-expert with the base model using different mixing coefficients (α) and plot their performance on the XSUM data and CNN/DM datasets in Figure 2. We choose to vary α from 0.0 to 1.0. We compare models on the Darc, E-Psrc, E-Rref , and ROUGE 1 metrics. We observe:
α works as a control knob for adjusting factual correctness and informativeness. As we increase the α, the performance of CaPEs on factuality metrics (Darc, E-Psrc) improves while it decreases on metrics of informativeness (E-Rref , ROUGE). Consequently, we can select the α that obtains the highest factual consistency while retaining ROUGE/ERref within the pre-defined tolerance level.
Intermixing an expert and an anti-expert based on different metrics provides the best performance trade-offs. CaPEDD, which uses
the DAE-based expert and anti-expert, improves Darc/Dsumm accuracy at the fastest rate on both datasets. Likewise, CaPEPP improves entity precision, E-Psrc, at the fastest rate. CaPEDP and CaPEPD models that intermix the expert and antiexpert based on different metrics provide the best bargain on all factual consistency metrics, evenly improving all Darc and E-Psrc scores. On the ROUGE score, we do not find any uniform pattern between the two datasets. On XSUM, all CaPE variants exhibit similar behavior while on CNN/DM, CaPEs using the entity precision-based anti-expert (CaPEPP/DP ) retain ROUGE better than their alternatives. Similarly, CaPEPP/DP retain entity recall better than their alternatives for all values of α on both datasets. Overall, CaPEDP provides the best balance for all performance measures on both datasets.

heading: 4.3 (Anti-)Expert Initialization: A Base Summarization Model outperforms BART
text: In Figure 3, we compare two variants of the CaPEPP model, the first initializes the expert (antiexpert) with the BART and the second with the base summarization model. First, we find that both models improve performance on all factual consistency metrics. On the E-Psrc metric, which was also used to select the training samples, both models obtain comparable improvements. However, on the DAE-based factual consistency metrics as well as ROUGE and E-Rref metrics, fine-tuning the base
model outperforms the one based on training BART. The gap in performance increases with the increase in value of α, i.e., when the influence of expert (anti-expert) increases. The performance difference is unsurprising, given that the re-trained model leads to lower ROUGE and information recall (Table 1) by being trained on fewer training samples. Secondly, training an expert model initialized with
BART takes a greater number of parameter updates (> 1 epoch) to reach the best performance on ROUGE and other metrics. In contrast, the base model already yields a higher ROUGE score, and fine-tuning it for 1 epoch is sufficient to reduce hallucinations, making fine-tuning a more efficient approach for building experts (anti-experts).

heading: 4.4 CaPE outperforms Simple Parameter Ensembling (WiSE-FT)
text: In Figure 4, we compare the CaPEPP model with the expert (anti-expert) only model that replaces the anti-expert (expert) with the base model in θCaPE . Accordingly, the expert only model is equivalent to the WiSE-FT formulation (θWiseFT ). While both the expert only and anti-expert only improve performance on factual consistency metrics, we observe that CaPEPP improves performance at a faster rate than the former two models. On ROUGE-1 and E-Rref scores, the CaPEPP performance lies in between the expert only and anti-expert only models. The performance variations for the three models indicate that the contrastive ensembling combines the gains from expert and anti-expert, helping us to effectively use both clean and noisy data.

heading: 5 Related Work
text: Abstractive text summarization metrics such as ROUGE (Lin, 2004) and BERTScore (Zhang et al.,
2019) evaluate lexical and semantic overlap respectively but fail to sufficiently evaluate factuality and faithfulness (Tejaswin et al., 2021). This has led to a line of research dedicated to evaluating factual consistency and hallucination in text summarization using new metrics such as entailment and question answering-based evaluation (Falke et al., 2019; Kryscinski et al., 2020; Maynez et al., 2020; Zhou et al., 2021; Eyal et al., 2019; Scialom et al., 2019; Wang et al., 2020; Durmus et al., 2020; Scialom et al., 2021). The research focused on comparing these factual consistency evaluation metrics (Gabriel et al., 2021; Fabbri et al., 2021a; Pagnoni et al., 2021; Goyal and Durrett, 2021; Tejaswin et al., 2021), however, often have contradicting observations. For instance, Durmus et al. (2020) found that entailment-based automated metrics have lower correlations with factual consistency while Pagnoni et al. (2021) concluded that the entailment-based FactCC exhibits the highest correlations with human judgments of factual consistency. Given the variations in findings from different human analyses of popular factual consistency evaluation metrics, we select a few metrics from each of the entailment, entity overlap, and QA-based evaluations, as well as use ROUGE and BERTScore metrics for evaluating CaPE.
Along with the growing body of work on the analysis and evaluation of factual consistency, there has been some recent work on developing methods to enforce factual consistency in pre-trained language models. These include sampling techniques such as constrained decoding (Mao et al., 2020) and neurologic decoding (Lu et al., 2020). Another strategy is to control generation either by using language models to guide a base language model as in GeDi (Krause et al., 2020) and DExperts (Liu et al., 2021a) or via a hallucination knob (Filippova, 2020). Although these methods claim to be generic, they have not been successfully applied to constrain summary generation on the source document.
Comparatively, there are fewer papers that propose methods for factual consistency in text summarization. Most of these focus on posthoc correction such as SpanFact (Dong et al., 2020), contrast entity generation and selection (Chen et al., 2021), loss truncation (Kang and Hashimoto, 2020; Goyal and Durrett, 2021), and encoding SRL structure (Cao et al., 2020). Aralikatte et al. (2021) uses focus attention and sampling to improve the diversity and faithfulness of summaries while Liu
et al. (2021b) uses data augmentation with a contrastive loss for factual consistency of abstractive summarization applied to customer feedback.
Finally, works focusing on data noise include revising hallucinated summaries in training data (Adams et al., 2022), dropping hallucinated samples (e.g. Nan et al. (2021) and Narayan et al. (2021) for summarization, Matsumaru et al. (2020) for headline generation), or defining curriculum based on the factual quality of training samples (Kano et al., 2021).

heading: 6 Conclusion
text: We present Contrastive Parameter Ensembling (CaPE) to reduce content hallucinations in abstractive summarization models. We first select clean (noisy) training samples to fine-tune an expert (antiexpert) model. Then, we use the difference between the parameters of expert and anti-expert models to adjust the parameters of a base summarization model. We evaluate CaPE on the XSUM and CNN/DM datasets using a diverse set of factual metrics, finding that CaPE effectively reduces hallucinations without a significant drop in ROUGE and information recall.
Limitations
The datasets utilized in this research contain documents and summaries in English (XSUM and CNN/DM datasets) and thus mainly represent the culture of the English-speaking populace. Gender, age, political or other biases may also exist in the dataset, and models trained on these datasets may propagate these biases.
Our experiments and analyses are based on the assumption that training data contains artifacts that lead to factual errors in summarization models. Also, it is evident from the results, that the effectiveness of our proposed models is relatively higher for the noisier XSUM dataset. So, our analytical results and improvement from a model may have limited implications on a perfect dataset that does not exhibit any learnable artifacts.
We relied on automated metrics, such as ROUGE and entity recall for measuring information relevance, and entity precision, question answeringbased metrics and dependency arc entailment accuracy for information correctness. These metrics are error-prone. Exclusively for a subset of models, that perform the best according to automated metrics, we use human annotations for additional
evaluations.

heading: A Experimental Details
text: Data Selection: For SELECTCLEAN (SELECTNOISY), we set ϵE−Psrcclean , ϵ DAEerror clean , ϵ E−Psrc noisy and ϵDAEerrornoisy to 1.0 (1.0), 0.0 (0.0), 0.75 (0.10) and 15.0 (15.0) respectively for CNN/DM (XSUM) dataset. In Table 5, we report the size of data subsets used for training experts and anti-experts used in our CaPE models.
We set the data selection thresholds for antiexperts based on their validation performance on their respective factuality metric. The plot for Darc and E-Psrc for AntiDAE and AntiE−P on XSUM and CNN/DM datasets are shown in Figure 5. The
size of datasets corresponding to different thresholds is shown in Table 6. We observe that selecting a small but noisiest subset of training data is the best strategy for training an anti-expert.
Training Experts (Anti-Experts): We use Huggingface Transformers library (Wolf et al., 2020) (PyTorch (Paszke et al., 2017)) to implement our experts (anti-experts). We initialize experts with the pre-trained summarization models (bart-largexsum, bart-large-cnn) and fine-tune them for 1 epoch with batch size of 64 using default train-
ing hyperparameters (optimizer: Adam, learning rate: 5e-5, β1: 0.9, β2: 0.999, ϵ: 1e-8). The experts (anti-experts) initialized with BART are trained for 5 epochs.
In Table 7, we report the α values for all variants of CaPE on XSUM and CNN/DM datasets.
Inference: We adopt the standard hyperparameters for all models during the inference, e.g. beam size of 6 (4), the minimum and maximum sequence length of 11 (56) and 62 (142), etc. for the XSUM (CNN-DM) model.
Experts (Anti-Experts) Performance on CNN/DM: In Table 8, we report the performance of experts and anti-experts on the CNN/DM dataset. They perform similarly to the experts (anti-experts) on the XSUM dataset.
Evaluation Metrics Details We use the DAE_xsum_human_best_ckpt and the CNNDM_synthetic/ENT-C_dae models for calculating DAE scores for the XSUM and CNN/DM models respectively. The hash for the BERTScorer used in our experiments is “roberta-large_L17_noidf_version=0.3.9(hug_trans=4.6.1)”

heading: B Sample Outputs
text: Effect of α: In Tables 9, we show examples to qualitatively illustrate the effect of α. In example 1, BART summary contains one factual error ( ‘a five-month experiment’ ) which gets removed in CaPEDP -generated summary if we set α to 0.40. As we further increase α, the summary doesn’t change in informativeness and retains its factual consistency. Similarly, in example 2, the BART summary contains one factual error, ‘10th anniversary’ . However, increasing α, in this case, couldn’t remove the factual error while preserving the informativeness. Rather, for α ≥ 0.8, CaPEDP removes the factual error but also changes the key information. Our qualitative analysis corroborates the empirical findings, where increasing α continues to improve the factuality but higher values lead to lower entity recall and ROUGE.
How CaPEDP differes from the BART? In Table 10, we show two examples where CaPEDP improves summary by completely removing factual errors. In examples 3, CaPEDP correctly removes an extrinsic entity error ‘20 people’ and an ex-
trinsic event error ‘killing’ without modifying the factually correct context. However, in example 4, while removing the factual error £1m , CaPEDP also removes its associated context ‘fraud at the charity he worked for was uncovered’. Through manual analyses, we find that CaPE models are susceptible to removing correct information. This is unsurprising since any model that aims to remove factual errors is susceptible to losing recall. However, as noted by the comparable performance of BART and CaPE models on E-Rref , the loss of information is negligible when evaluated on the entire test set.
In Table 11, we show examples where CaPEDP only partially removes the error. In example 5, CaPEDP correctly replaces an entity error India’s railways minister with the correct
entity a senior Maoist leader but it failed to correct the error Maoist rebels . In example 6, BART summary contains two extrinsic errors Seamus and at the age of 60 . To correct these errors, CaPEDP preferably replaces ‘at the age of 60’ with another important information ( starred in the RTE soap opera Glenroe ) from the source article. However, while CaPEDP correctly identifies the error Daithi , it replaces that with
another extrinsic error Seamus . The best possible correction strategy for the error Daithi would have been to simply remove it. However, CaPE inherits the tendency of BART models to generate certain types of information (e.g. First Name) irrespective of whether that information is inferable from the source article.
Example 1 The six ""astronauts"" wearing bright blue jump-suits and even surgical masks, were paraded before banks of television cameras and hordes of journalists at a news conference before entering their mock spaceship.Amongst the long rows of VIPs at the news conference were senior officials from the United States, China and the European Union.If, as some experts believe, the main aim of the Mars 500 experiment is to publicise the concept of human flight to the red planet, then it has surely succeeded beyond all expectations.""I am very happy to be part of this project,"" said Diego Urbina, the Colombian-Italian and most extrovert member of the crew.""It will raise awareness of space flight so hopefully a few years from now there will be a real flight to Mars.""He confessed that Elton John had been his inspiration.""I don’t know if you know that song Rocket Man,"" he asked.""I want a future like thatâC¦ where people will be going frequently into space and will be working there and it will be very usual.""In front of the world’s media, all the team spoke confidently about the chances of the experiment being successful - in other words that noone would crack under the stress of such lengthy confinement in such claustrophobic and bizarre conditions and demand to be let out.""The target is for all six of us to be here for 520 days,"" said the French crew-member Romain Charles who took a guitar with him into the cluster of brown and silver-coloured metal tubes which will be home until November 2011.After the news conference, the six crew disappeared, re-emerging an hour later by the entrance hatch to the mock spaceship, where they put on another high-spirited performance for the media.Finally, blowing kisses and waving to wives, girlfriends and relatives, they walked up the steps and through the entrance hatch.A solemn-faced official slowly closed and sealed it behind them.So now reality bites for the six-member volunteer crew.What will they be thinking as they sit inside their tin cans in north-west Moscow where outside the warm sun shines and the flowers blossom?There is no thrill of a blast-off and flight through space.There are no windows from which to watch the Earth gradually shrink away.And no anticipation of reaching a new world more than fifty million kilometres away.Instead, silent inertia, stale air and tinned food.And everywhere cameras watching their every move, looking out for signs of mental collapse.They have just one thing to cling on to, that they are playing their part in the history of space exploration.That their success in this experiment will mean a human flight to Mars is a step closer.And space experts already believe the first flight could be just 25 years away or even less if there is the political and economic will from countries with advanced space programmes. BART: The crew of the Mars 500 experiment have arrived in Moscow for the start of a five-month experiment in which they will spend 520 days locked inside tin cans. CaPEDP (α=0.20): The crew of the Mars 500 experiment have arrived in Moscow for the start of a five-month experiment in which they will spend 520 days locked inside tin cans. CaPEDP (α=0.40): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=0.60): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=0.80): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=1.0): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. Example 2 The submarine, one of the Russian navy’s most advanced vessels, sank in the Barents Sea on 12 August, 2000 with the loss of all 118 people on board.An explosion of fuel from an old torpedo caused the disaster.Moscow’s response to one of the greatest disasters in Russian naval history was widely criticised.Relatives and members of Russia’s northern fleet are due to cast wreaths into the sea on Thursday in memory of the crew.Flags are being flown at half-mast at the headquarters of all Russia’s naval fleets, and a ceremony and minute’s silence was being held at Moscow’s Central Army Museum.The initial response to the disaster in 2000 was shambolic, says the BBC’s Richard Galpin.After radio contact was lost there was a still unexplained delay before a search and rescue mission was launched.Although the submarine was lying just 100m below the surface of the sea, attempts to locate it and reach it repeatedly failed.It was days before the authorities informed relatives that something was wrong and the then President, Vladimir Putin, initially remained on holiday.Russia eventually accepted international assistance, but when Norwegian divers opened the Kursk’s hatch 10 days later they found the boat flooded and everyone dead.Many had died within seconds of the initial explosion, but others survived for several hours, a report found.Russian officials originally suggested the submarine may have collided with a foreign ship or with a stray mine.But it emerged that an explosion was caused by fuel that had leaked from a torpedo.This started a fire, which subsequently caused all ammunition on board to detonate.The boat was raised and the bodies recovered in 2001. BART: Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0.20): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0.40): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0.60): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0.80): A minute’s silence is being held in Moscow to remember the crew of the Russian submarine
Kursk, which sank in 2000. CaPEDP (α=1.0): A minute’s silence is being held in Moscow to remember the crew of the Russian submarine
Kursk, which sank in 2000.
Table 9: Examples: Effect of α on summary factuality and informativeness.
Example 5 Comrade Akaash’s statement comes after the rebels were blamed for Friday’s train crash which left 148 people dead.Police say Maoist rebels sabotaged the track, causing the derailment of the Calcutta-Mumbai express in West Bengal.Maoists denied the charge. But Comrade Akaash also said they would investigate whether any rebels were involved.Railway officials in eastern India have cancelled night trains in Maoist-affected areas after Friday’s incident.Comrade Akaash told the BBC that they were "appealing" to the railways to run trains through rebel strongholds even during the night.Profile: India’s Maoist rebelsIn pictures: India train collision"We are promising total security to all trains. We will not allow anyone to attack any train anywhere in the country and those trying to do it will face stern punishment," he said.The railways have not reacted to the statement.Police say they have "definite evidence" that a local rebel Maoist militia were behind the disaster - they have named two militia leaders as the prime suspects.One of the suspects, Umakanta Mahato, was arrested last June and charged with sedition and waging war against the state.But he was released on bail in December, and the police did not contest the bail, court records say.Independent lawyers are asking why the police did not contest the bail plea of a senior Maoist militia leader.Railway officials in eastern India have cancelled night trains in Maoist-affected areas after Friday’s incident.The restrictions would be in place until 0500 [2330GMT] on 3 June, the company said.Report said other services were being rescheduled to ensure they travelled through Maoist areas of eastern India in daylight.Prime Minister Manmohan Singh has described the Maoist insurgency as India’s biggest internal security challenge. BART: Maoist rebels should be allowed to run trains through their strongholds even during the night, India’s railways minister has told the BBC.
CaPEDP : Maoist rebels in India should be allowed to run trains through their strongholds during the night, a senior Maoist leader has told the BBC. Example 6 He passed away peacefully in hospital on Tuesday after a short illness.Born in Tourmakeady, County Mayo, he worked as a teacher before securing a part in the premiere of the Brian Friel play Translations in 1980.Lally became a household name in Ireland for his role as Miley Byrne in the RTE soap opera Glenroe and later starred in the BBC series Ballykissangel.He also appeared in the Hollywood movie Alexander and provided the voice for the Oscar-nominated, animated Irish film, The Secret of Kells.As a fluent Irish speaker and advocate of the language, Lally had roles in several Irish language films.He is survived by his wife Peggy and their children Saileog, Darach and Maghnus. BART: Irish actor and director Daithi Lally has died at the age of 60 . CaPEDP : Irish actor Seamus Lally, who starred in the RTE soap opera Glenroe, has died.
Table 11: Examples: CaPEDP improves factuality by replacing factual errors.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: A1. Did you describe the limitations of your work?
text: Left blank.

heading: A2. Did you discuss any potential risks of your work?
text: Left blank.

heading: A3. Do the abstract and introduction summarize the paper’s main claims?
text: Left blank.

heading: A4. Have you used AI writing assistants when working on this paper?
text: Left blank.

heading: B Did you use or create scientific artifacts?
text: Left blank.

heading: B1. Did you cite the creators of artifacts you used?
text: Left blank.

heading: B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Left blank.

heading: B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank.

heading: B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank.

heading: B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Left blank.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank.

heading: C Did you run computational experiments?
text: Left blank.

heading: C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Left blank.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Left blank.

heading: C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank.
C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank.

heading: D Did you use human annotators (e.g., crowdworkers) or research with human participants?
text: Left blank.

heading: D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Left blank.

heading: D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Left blank.

heading: D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? Left blank.

heading: D4. Was the data collection protocol approved (or determined exempt) by an ethics review board?
text: Left blank.

heading: D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: Left blank.

heading: Future Work
text: ""I want a future like thatâC¦ where people will be going frequently into space and will be working there and it will be very usual. ""In front of the world’s media, all the team spoke confidently about the chances of the experiment being successful - in other words that noone would crack under the stress of such lengthy confinement in such claustrophobic and bizarre conditions and demand to be let out. ""The target is for all six of us to be here for 520 days,"" said the French crew-member Romain Charles who took a guitar with him into the cluster of brown and silver-coloured metal tubes which will be home until November 2011. After the news conference, the six crew disappeared, re-emerging an hour later by the entrance hatch to the mock spaceship, where they put on another high-spirited performance for the media. Finally, blowing kisses and waving to wives, girlfriends and relatives, they walked up the steps and through the entrance hatch. A solemn-faced official slowly closed and sealed it behind them. So now reality bites for the six-member volunteer crew. What will they be thinking as they sit inside their tin cans in north-west Moscow where outside the warm sun shines and the flowers blossom?There is no thrill of a blast-off and flight through space. There are no windows from which to watch the Earth gradually shrink away. And no anticipation of reaching a new world more than fifty million kilometres away. Instead, silent inertia, stale air and tinned food. And everywhere cameras watching their every move, looking out for signs of mental collapse. They have just one thing to cling on to, that they are playing their part in the history of space exploration. That their success in this experiment will mean a human flight to Mars is a step closer. And space experts already believe the first flight could be just 25 years away or even less if there is the political and economic will from countries with advanced space programmes. BART: The crew of the Mars 500 experiment have arrived in Moscow for the start of a five-month experiment in which they will spend 520 days locked inside tin cans. CaPEDP (α=0. 20): The crew of the Mars 500 experiment have arrived in Moscow for the start of a five-month experiment in which they will spend 520 days locked inside tin cans. CaPEDP (α=0. 40): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=0. 60): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=0. 80): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. CaPEDP (α=1. 0): The crew of the Mars 500 experiment have arrived in Moscow to spend 520 days locked inside a series of tin cans. Example 2 The submarine, one of the Russian navy’s most advanced vessels, sank in the Barents Sea on 12 August, 2000 with the loss of all 118 people on board. An explosion of fuel from an old torpedo caused the disaster. Moscow’s response to one of the greatest disasters in Russian naval history was widely criticised. Relatives and members of Russia’s northern fleet are due to cast wreaths into the sea on Thursday in memory of the crew. Flags are being flown at half-mast at the headquarters of all Russia’s naval fleets, and a ceremony and minute’s silence was being held at Moscow’s Central Army Museum. The initial response to the disaster in 2000 was shambolic, says the BBC’s Richard Galpin. After radio contact was lost there was a still unexplained delay before a search and rescue mission was launched. Although the submarine was lying just 100m below the surface of the sea, attempts to locate it and reach it repeatedly failed. It was days before the authorities informed relatives that something was wrong and the then President, Vladimir Putin, initially remained on holiday. Russia eventually accepted international assistance, but when Norwegian divers opened the Kursk’s hatch 10 days later they found the boat flooded and everyone dead. Many had died within seconds of the initial explosion, but others survived for several hours, a report found. Russian officials originally suggested the submarine may have collided with a foreign ship or with a stray mine. But it emerged that an explosion was caused by fuel that had leaked from a torpedo. This started a fire, which subsequently caused all ammunition on board to detonate. The boat was raised and the bodies recovered in 2001. BART: Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0. 20): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0. 40): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0. 60): Russia is marking the 10th anniversary of the sinking of the Kursk submarine. CaPEDP (α=0. 80): A minute’s silence is being held in Moscow to remember the crew of the Russian submarine
Kursk, which sank in 2000. CaPEDP (α=1. 0): A minute’s silence is being held in Moscow to remember the crew of the Russian submarine
Kursk, which sank in 2000. Table 9: Examples: Effect of α on summary factuality and informativeness. Example 5 Comrade Akaash’s statement comes after the rebels were blamed for Friday’s train crash which left 148 people dead. Police say Maoist rebels sabotaged the track, causing the derailment of the Calcutta-Mumbai express in West Bengal. Maoists denied the charge. But Comrade Akaash also said they would investigate whether any rebels were involved. Railway officials in eastern India have cancelled night trains in Maoist-affected areas after Friday’s incident. Comrade Akaash told the BBC that they were "appealing" to the railways to run trains through rebel strongholds even during the night. Profile: India’s Maoist rebelsIn pictures: India train collision"We are promising total security to all trains. We will not allow anyone to attack any train anywhere in the country and those trying to do it will face stern punishment," he said. The railways have not reacted to the statement. Police say they have "definite evidence" that a local rebel Maoist militia were behind the disaster - they have named two militia leaders as the prime suspects. One of the suspects, Umakanta Mahato, was arrested last June and charged with sedition and waging war against the state. But he was released on bail in December, and the police did not contest the bail, court records say. Independent lawyers are asking why the police did not contest the bail plea of a senior Maoist militia leader. Railway officials in eastern India have cancelled night trains in Maoist-affected areas after Friday’s incident. The restrictions would be in place until 0500 [2330GMT] on 3 June, the company said. Report said other services were being rescheduled to ensure they travelled through Maoist areas of eastern India in daylight. Prime Minister Manmohan Singh has described the Maoist insurgency as India’s biggest internal security challenge. BART: Maoist rebels should be allowed to run trains through their strongholds even during the night, India’s railways minister has told the BBC. CaPEDP : Maoist rebels in India should be allowed to run trains through their strongholds during the night, a senior Maoist leader has told the BBC. Example 6 He passed away peacefully in hospital on Tuesday after a short illness. Born in Tourmakeady, County Mayo, he worked as a teacher before securing a part in the premiere of the Brian Friel play Translations in 1980. Lally became a household name in Ireland for his role as Miley Byrne in the RTE soap opera Glenroe and later starred in the BBC series Ballykissangel. He also appeared in the Hollywood movie Alexander and provided the voice for the Oscar-nominated, animated Irish film, The Secret of Kells. As a fluent Irish speaker and advocate of the language, Lally had roles in several Irish language films. He is survived by his wife Peggy and their children Saileog, Darach and Maghnus. BART: Irish actor and director Daithi Lally has died at the age of 60. CaPEDP : Irish actor Seamus Lally, who starred in the RTE soap opera Glenroe, has died. Table 11: Examples: CaPEDP improves factuality by replacing factual errors. ACL 2023 Responsible NLP Checklist