SlowGuess/NAACL_2022_OCR huggingface dataset:
FACTPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
David Wan and Mohit Bansal  
University of North Carolina at Chapel Hill  
{davidwan,mbansal}@cs.unc.edu
We present FACTPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and finetuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 2020) pre-training objective to create pseudosummaries that are both important and factual; (2) We introduce three complementary components for fine-tuning. The corrector removes hallucinations present in the reference summary, the contractor uses contrastive learning to better differentiate nonfactual summaries from factual ones, and the connector bridges the gap between the pre-training and finetuning for better transfer of knowledge. Experiments on three downstream tasks demonstrate that FACTPEGASUS substantially improves factuality evaluated by multiple automatic metrics and humans. Our thorough analysis suggests that FACTPEGASUS is more factual than using the original pre-training objective in zero-shot and few-shot settings, retains factual behavior more robustly than strong baselines, and does not rely entirely on becoming more extractive to improve factuality.
Abstractive summarization aims at generating short summaries that capture the essentials of a long document. Research in this challenging task has made significant progress with the help of large pre-trained models (Lewis et al., 2020; Raffel et al., 2020; Zhang et al., 2020). However, current models suffer from the crucial problem of hallucinations (Maynez et al., 2020), where a summary contains facts or entities not present in the original document. Such unfaithful generation raises the question of whether the models can be trustworthy and used safely for real-world applications. To tackle this problem, many approaches propose post-processing models (Chen et al., 2021; Dong et al.,
2020; Liu and Liu, 2021), but such methods are often constrained by external resources to train additional correction or selection models. An alternative line of works focuses on learning factuality directly during fine-tuning by filtering nonfactual training data (Goyal and Durrett, 2021; Nan et al., 2021) or, most recently, incorporating contrastive learning (Cao and Wang, 2021) to encourage generating faithful summaries.
In this work, we propose FACTPEGASUS, a model that addresses the problem of hallucinations for abstractive summarization holistically, by incorporating factuality into the whole training pipeline: We tackle the lack of factuality objective in pre-training and the presence of hallucinations in the downstream dataset during fine-tuning. Current pre-training objectives focus on improving the quality of the generated output in the downstream tasks but often overlook the factuality aspect. Thus, we explore incorporating factuality into the pre-training objective of PEGASUS (Zhang et al., 2020) (a state-of-the-art abstractive summarization model). The original objective, gap sentence generation (GSG), transforms any text into a pseudo-summarization dataset by selecting important sentences using ROUGE (Lin, 2004) as output summaries. We explore strategies for combining ROUGE and the factuality metric FactCC (Kryscinski et al., 2020) as the selection criteria, so that the model learns to generate sentences that cover the most important information of the input document as well as remain faithful to it.
Next, we propose three complementary modules that further address factuality problems during fine-tuning: (1) Corrector that removes hallucinations existing in reference summaries, allowing training on the full training set without learning unfaithful behaviors; (2) Contrastor that encourages the model to better differentiate factual summaries from nonfactual ones by paying attention to the document using contrastive learning; (3) Connec
1Our code and data are publicly available at: https://github.com/meetdavidwan/factpegasus.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1010 - 1028 July 10-15, 2022 ©2022 Association for Computational Linguistics
tor, a special mask-token fine-tuning technique enabled by the GSG-style objective, that simulates the pre-training task during fine-tuning by inserting the mask token into the input document so that the pre-trained model can adapt its knowledge of generating factual summaries directly to the downstream tasks. The connector, corrector, and contractor address the input, output, and training objective of the downstream task, respectively, and the combination of the components reduces potential confounding problems that cannot be addressed by a single module. We show that the full model improves three factuality metrics, the token and sentence error of DEP Entail (Goyal and Durrett, 2021) and FactCC, on the downstream datasets of XSum (Narayan et al., 2018), WikiHow (Koupaee and Wang, 2018), and Gigaword (Rush et al., 2015). Most notably, FACTPEGASUS outperforms existing factuality-aware summarization models by more than  $40\%$  and  $34\%$  on XSum for token error and FactCC, respectively. Ablation studies show the usefulness of each of our fine-tuning components as well as the additive gain of combining our complementary modules, and human evaluation confirms that FACTPEGASUS generates significantly more factual summaries over strong baselines.
Finally, we perform a detailed analysis of FACT-PEGASUS, which points to several important observations regarding learning and maintaining factuality: (1) Zero-shot setting demonstrates the utility of our factuality-aware pre-training objective, as our model outperforms PEGASUS (which uses the original objective) on all three factuality metrics when evaluated directly on the downstream task without any supervised training data. Few-shot experiment indicates that even a small number of nonfactual examples can have a strong negative impact on factuality and can nullify much of the gain from factuality pre-training, highlighting the importance of ensuring factuality during fine-tuning. (2) Factuality dynamics (Goyal et al., 2022) further shows that FACTPEGASUS exhibits a lesser degree of factuality degradation than what is observed for BART-base. (3) Factuality vs abstractiveness tradeoff curve reveals that FACTPEGASUS effectively improves factuality by not simply relying on the increase in extractiveness.
1. We propose a factuality-aware pre-training objective for abstractive summarization and study the effect of different sentence selection strate
gies on downstream factuality.
2. We introduce three complementary components for improving factuality during fine-tuning that correct hallucinations present in the training set, discourage unfaithful generation during training, and bridge the gap between pre-training and finetuning. The full model consistently achieves better factuality scores than strong baselines on three downstream abstractive summarization tasks, confirmed by human evaluation.
3. We conduct thorough factuality analysis and show that FACTPEGASUS generates more factual summaries with no or little supervision, slows down factuality degradation observed for current models, and improves factuality not by becoming more extractive.
Pre-training Objective for Generation Tasks. Transformer-based models have achieved state-of-the-art performance for abstractive summarization (Devlin et al., 2019; Lewis et al., 2020; Raffel et al., 2020; Zhang et al., 2020). Many such pre-trained models study the effect of useful pre-training objectives, often in the form of masking certain parts of the input. BART (Lewis et al., 2020) randomly masks spans of tokens in the text as input and asks the model to reconstruct the original text. Our work builds on the success of PEGASUS's (Zhang et al., 2020) pre-training objective that closely resembles the downstream summarization task. Their objective selects sentences that best represent the document as the output summary, and masks out the selected sentences in the original text as the input document. We explore various sentence selection strategies to encourage the model to generate summaries that cover the most important information of the document and also remain faithful to it.
Improving Factuality for Summarization. Recent models can achieve highly fluent and coherent abstractive summaries, yet the generated summaries often contain factual errors (Falke et al., 2019; Maynez et al., 2020). Several approaches have addressed this problem, which can be roughly categorized into two types. The first approach proposes post-processing models, that either removes hallucinations in the generated summaries (Cao et al., 2020; Dong et al., 2020), or selects the most factual candidate during beam search (Chen et al., 2021). This approach often requires training addi
tional models and external resources. In an attempt to improve factuality in an end-to-end fashion, Nan et al. (2021) and Goyal and Durrett (2021) explore a useful method of removing nonfactual examples during training, but this only allows the model to be trained on a small portion of the training data.
Recently, contrastive learning (Chopra et al., 2005, CL) has started to gain traction for improving factuality. Popular for representation learning, CL has had great success for vision tasks (Chen et al., 2020) and has also been successfully applied to summarization, where Liu and Liu (2021) improves summary quality by differentiating high-quality summaries from the lower-quality ones. Cao and Wang (2021) extend this idea to improve factuality with various approaches to generate hallucinated summaries as negative examples, showing consistent improvement over existing methods. We similarly incorporate CL as an additional training objective, but we differ from previous works in the choice of anchor and positive sample. Inspired by Lee et al. (2021), who use encoder and decoder output as candidates for CL across multiple text generation tasks, we extend this idea to factuality, i.e., instead of performing CL only between summaries, we perform CL between the document and the summary. This setup encourages the model to generate a faithful summary that pays attention to the document, i.e., the definition of faithfulness.
3 FACTPEGASUS
We describe our training procedure consisting of pre-training with a factuality-aware objective (Section 3.1) and fine-tuning with three complementary modules for improving factuality (Section 3.2).
3.1 Factuality-Aware Pre-training
Recent exploration of good pre-training objectives for abstractive summarization aims at achieving high quality on downstream tasks, often in terms of ROUGE. However, few have analyzed the effect of pre-training objective on factuality. We focus on incorporating this aspect into the pre-training objective of PEGASUS, gap sentence generation (GSG), since PEGASUS achieves state-of-the-art performance on the downstream abstractive summarization tasks. The GSG objective transforms text documents into a pseudo-summarization dataset by selecting important sentences as the output summary, which are subsequently masked out in the original text. The best strategy determines the importance
by calculating ROUGE-1 between each chosen sentence and the rest of the document. While the original strategy selects sentences that contain the most unigram overlap, there is no guarantee that the selected sentences are faithful to the rest of the document. We provide an illustrative example in Figure 1a, where the original objective selects sentence C due to its high ROUGE-1 score. However, this sentence is not a faithful summary to the rest of the document as the other sentences concern with the fire in Seattle while only sentence C talks about the fire in Denver.
To address this problem, we extend this objective, which we call factual GSG (factGSG), where we additionally measure the importance of the sentences according to factuality. We use FactCC (Kryscinski et al., 2020) as the factuality criteria when selecting the summary sentences, as it correlates highly with human factuality judgment (Pagnoni et al., 2021) and is relatively fast to compute. FactCC produces a binary prediction where a score of 1 indicates that the selected sentence is consistent with the rest of the document. Another change in factGSG is the choice of gap sentence ratio, which determines the percentage of sentences in the text that will be selected as the summary. Instead of selecting  $30\%$  of the text document as output summary, we only select one sentence, as selecting more sentences will inevitably increase the possibility of hallucinations.
Formally, given a document  $D$  of  $n$  sentences,  $D = \{x_{1},x_{2},\dots,x_{n}\}$ , we select the top-scoring sentence as the output summary, where the score of each sentence  $x_{i}$  is calculated by:
$$
s _ {i} = \operatorname {r o u g e} \left(x _ {i}, D \backslash \left\{x _ {i} \right\}\right) + \operatorname {F a c t C C} \left(x _ {i}, D \backslash \left\{x _ {i} \right\}\right)
$$
Going back to the example in Figure 1a, FactCC assigns a score of 0 to the nonfactual sentence C because the fire in Denver is not entailed by the other sentences. This results in sentence A scoring higher than the nonfactual sentence, and thus overcomes the problem in the original objective.
3.2 Factuality-Aware Fine-tuning
Although the typical approach of updating all the model's parameters during fine-tuning adapts well to the downstream task, the model suffers from imitative falsehood (Lin et al., 2021): The model learns to generate similar hallucinations present in the downstream dataset, and even completely forgets its factual behaviors learned during pretraining. This is especially problematic for datasets
(a) Pre-training
(b) Fine-tuning
Figure 1: Illustration of FACTPEGASUS. For pre-training (a), we use the factGSG objective introduced in Section 3.1 that transforms a text document into a pseudo-summarization dataset. We select the pseudo-summary using the combination of ROUGE and FactCC. Here, sentence A is selected as the pseudo-summary, and we mask this sentence in the original text to create the pseudo-document. During fine-tuning (b), the connector (i) simulates the factGSG task by appending the same mask token used in (a) to the input document, so that we have the same setup in both training stages. Then, corrector (ii) removes hallucinations (highlighted in red) from the summary. Finally, contrastive learning in (iii) encourages the model to prefer the corrected summary over the perturbed summary.
like XSum that contains hallucinations on  $70\%$  of the summaries (Maynez et al., 2020).
To this end, we present three complementary fine-tuning modules, illustrated in Figure 1b. Each component addresses different parts of the downstream task and collaboratively ensures factuality throughout the fine-tuning stage.
3.2.1 Connector
The GSG objective enables faster and better adaptation during fine-tuning by simulating the downstream task (Zhang et al., 2020). However, there still exists a gap between pre-training and fine-tuning: GSG is a masked sentence prediction task, but downstream summarization does not make use of the mask token. Thus, we simply insert the mask token into the input document of the downstream dataset, so as to simulate what the model expects during pre-training. This can be seen as a form of prompting, which helps us to elicit the factuality knowledge of the pre-trained models. We insert the mask token between sentences, and the best position is determined by evaluating the summarization performance on the validation set. We report the best position of the mask token and discuss the similarity to prompting in Appendix C.
3.2.2 Corrector
The corrector removes hallucinations in the reference summaries so that such examples can be used during training without contributing to the problem of imitative falsehood. We consider summary entities as hallucinating if the text cannot be matched to one of the document entities. We propose three approaches with varying degrees of aggressiveness
w.r.t. the removal of hallucinations and the possibility of generating ungrammatical sentences.
Replace: Upon qualitative analysis, we discover that some hallucinated entities in the summary are partially present in the documents. The most prominent example is the use of names, where the summary contains the full name of the person while only the first or last name is mentioned in the document, as shown in Figure 2. Given such observation, we propose a method to find a similar entity with the same NER label in the document and use that to replace the original hallucinated entity in the summary. Although this approach cannot correct hallucinations where similar entities are missing in the document, grammaticality is ensured.
Remove: A more aggressive approach is to remove the hallucinated entities in the training examples. The intuition is that it is often better to not say anything than to say something wrong. We mitigate the problem of creating ungrammatical sentences by removing related words to the removed entities determined by dependency arcs.
Combined: As a middle ground that ensures no hallucinations are present in the reference summaries while being grammatical when possible, we first replace all possible entities and then apply the remove strategy on the remaining ones.
We refer the readers to Appendix-B.1 for the details about hallucination detection, as well as the algorithm and discussion of grammatically for the remove method.
Document	[Arteta]ent, [34]number, retired from playing at [the end of last season]date ... [Arteta]ent was seen crying after his final [Arsenal]ent match... [Guardiola]ent's first game since succeeding [Manuel Pellegrini]ent ...
Summary	Former [Arsenal]ent midfielder [Mikel Arteta]ent has taken up a coaching role at [Manchester City]ent.
Corrector
Replace	Former Arsenal midfielder Arteta has taken up a coaching role at Manchester City.
Remove	Former Arsenal midfielder Mikel Arteta has taken up a coaching role at Manchester City.
Combined	Former Arsenal midfielder Arteta has taken up a coaching role at Manchester City.
Contrastor
Intrinsic	Former Arsenal midfielder Manuel Pellegrini has taken up a coaching role.
Extrinsic	Former Arsenal midfielder Wenger has taken up a coaching role.
Figure 2: Example output using different strategies of corrector and contractor. The first two rows show the original document and summary with highlighted entities and their respective labels (date, number, ent). We mark hallucinated entities in the summaries with red, factual entities in document and summary with green and underlined, and removed entities by the corrector with a striking through. Perturbed entities by the contractor are italicized.
3.2.3 Contrastor
To better distinguish factual summaries from nonfactual ones, we next introduce a contrastive learning objective that encourages the model to prefer factual summaries given the context of the document. We use the document  $D_{i}$  as the anchor and only consider the reference summary  $S_{i}$  as the positive sample. Then, we create a set of nonfactual summaries  $N_{i}$  to form negative pairs following Kryscinski et al. (2020), where we replace factual entities with random entities of the same named entity labels. We experiment with two variants simulating either extrinsic and intrinsic hallucinations. As formulated in Maynez et al. (2020), extrinsic hallucinations refer to entities that are present in the summary but not in the document, whereas intrinsic hallucinations are those that are present in the document but contain inaccurate information or are misplaced. See Appendix B.2 for more details.
We stress that we perform contrastive learning between the document and the summary, similar to Lee et al. (2021), instead of between summaries (Cao and Wang, 2021), as it follows closer to the definition of faithfulness - the summary should be generated within the context of the document.
We use the NT-Xent loss (Chen et al., 2020):
$$
l _ {D _ {i}, S _ {i}} = - \log \frac {\exp \sin (z _ {D _ {i}} , z _ {S _ {i}}) / \tau}{\sum_ {S _ {j} \in N _ {i} \cup \{S _ {i} \}} \exp \sin (z _ {D _ {i}} , z _ {S _ {j}}) / \tau}
$$
where  $z_{D_i}, z_{S_i}$  and  $z_{S_j}$  are representation for  $D_i$ ,  $S_i$  and  $S_j$ , respectively. We generate  $z_D$  and  $z_S$  by performing mean pooling over the last hidden layer of the encoder and decoder output, respectively.  $\sin (\cdot ,\cdot)$  is the cosine similarity between the representations, and  $\tau$  is the temperature parameter.
The final loss is calculated by the sum of the cross-entropy loss  $L_{CE}$  and the contrastive loss:  $L = L_{CE} + \lambda L_{CL}$ , where  $\lambda$  is a scalar.
Figure 3: Relative effect using different sentence selection criteria on XSum. Adding FactCC to criteria consistently improves factuality. Full result in Table 10.
We describe our experimental setup, and refer to Appendix A for more details.
We pre-train on the C4 dataset (Raffel et al., 2020), and evaluate our pre-trained model on three downstream abstractive summarization datasets: XSum (Narayan et al., 2018), WikiHow (Koupaee and Wang, 2018), and Gigaword (Rush et al., 2015). XSum is the primary dataset for analysis unless otherwise stated, as most of the factuality works for abstractive summarization evaluate on this dataset. Dataset details are presented in Appendix A.1.
We report ROUGE-L (Lin, 2004) to evaluate our generated summaries against the reference. However, we note that this method is not ideal given the presence of hallucinations in the reference summaries (Chen et al., 2021; Maynez et al., 2020): If a more factual model does not produce such hallucinations, the output is scored lower than those
that contain the same hallucinations found in the reference.
To evaluate factuality, there have been many proposed automatic metrics (Durmus et al., 2020; Wang et al., 2020; Scialom et al., 2021). We report FactCC (Kryscinski et al., 2020) and DEP-Entail (Goyal and Durrett, 2021), as they are highly correlated with human judgment of factuality (Pagnoni et al., 2021). For DEP-Entail, we report the token-level and sentence-level error. For FactCC, since the model has been trained to evaluate on single sentences, we calculate the average score across all sentences for each summary.
To confirm our observation, we conduct human evaluation asking Amazon Mechanical Turk² (AMT) to judge the factuality and informativeness of the summaries. We randomly select 100 documents and ask the annotators to check whether each of the generated summaries is factual and informative. Appendix E provides more details.
4.2 Pre-training and Fine-tuning Setup
For pre-training, we use BART-base's architecture with PEGASUS's SentencePiece (Kudo, 2018) unigram model tokenizer. We first determine the best sentence selection criteria by experimenting with selection criteria that use ROUGE-1, ROUGE-2, and ROUGE-L, as well as combining each with FactCC. To save computation (Lewis et al., 2020; Zhang et al., 2020; Raffel et al., 2020), we pretrain these models on a smaller dataset and fewer training steps. We report the effect of the selection criteria using the normalized ROUGE score and factuality scores over the model that uses ROUGE-1 as the selection criteria. We take the complement of token error and sentence error as token accuracy and sentence accuracy, respectively, to present all metrics where higher is better. Details of pretraining are shown in Appendix A.4.
Finally, We evaluate our pre-trained model on the three downstream tasks. As baselines, we compare our model to BART-base and PEGASUS*, our variant of the PEGASUS-base as there is no publicly available checkpoint. We train PEGASUS* by using the original sentence selection metric (ROUGE-1), and observe higher ROUGE scores on XSum and WikiHow than the ones reported in the original paper. We also compare FACTPEGASUS to two summarization models optimized for factuality. DAE (Goyal and Durrett, 2021) uses