Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency
Year: 2023

Abstract:
In this study, we analyze the model intrinsic features of a summarization model by varying the fine-tuning objectives and datasets. We finetune BART models combining three fine-tuning objectives (negative log-likelihood, unlikelihood, and contrastive loss) and two datasets (CNN/DailyMail and XSum) and provide shuffled or aligned documents to observe changes in the model predictions and intrinsic features. We find that (i) the inductive bias for factual consistency during the fine-tuning procedure depends on both the objectives and datasets, and (ii) summarization models with relatively low factual consistency are more likely to model summaries that are not conditional to the documents. We demonstrate that splitting data based on the unconditional and conditional summary modeling difficulty affects the factual consistency and intrinsic features of the summarization models. Our experimental results highlight the importance of studying the inductive bias during fine-tuning for factual consistency.

heading: 1 Introduction
text: Factual consistency in summarization denotes whether the facts in the generated summary are consistent with those of the given document. Factual consistency is essential but remains challenging, particularly in abstractive summarization (Cao et al., 2018; Dong et al., 2020; Huang et al., 2020). Recently, the fine-tuning of pre-trained language models has resulted in an excellent performance with improved factual consistency (Zhang et al., 2020; Cao and Wang, 2021; Wan and Bansal, 2022).
In fine-tuning based summarization, fine-tuning objectives such as contrastive loss (CL) are combined with cross-entropy loss to improve informativeness or factual consistency (Liu and Liu, 2021; Cao and Wang, 2021; Wan and Bansal, 2022). They
∗ Corresponding author
focus on improving the informativeness and factual consistency aspects of the generated summaries.
Factual consistency also depends on the finetuning datasets. In particular, models trained with XSum (Narayan et al., 2018), where the reference summaries are highly abstractive, are known to show degenerated factual consistency (Kryscinski et al., 2020; Maynez et al., 2020; Xie et al., 2021).
There have been several studies on the properties of datasets in this context (Kryscinski et al., 2019; Kang and Hashimoto, 2020; Bommasani and Cardie, 2020; Wan and Bansal, 2022; Liu et al. (2022); Cao and Wang, 2021). For instance, Lin et al. (2022) and Wan and Bansal (2022) report that models trained using XSum are more likely to generate hallucinated words, which is critical to the factual consistency. Liu et al. (2022) inspect the dis-
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tribution of predicted hallucinations in terms of the probability and entropy of model predictions. Kang and Hashimoto (2020) propose a loss truncation approach based on the observation that samples with hallucinations have higher losses than other samples. Previous works have focused on dataset intrinsic features or model predictions, but the inductive bias from the datasets and model intrinsic features with respect to the factual consistency has not been fully explored.
In this paper, we propose a unified view of the intrinsic features of a fine-tuning based summarization model by integrating the aspects of finetuning objectives and datasets. We utilize BART (Lewis et al., 2020), which has recently been used in fine-tuning based summarization models for factual consistency. We compare three training objectives: negative log-likelihood (NLL), unlikelihood (UL, Welleck et al., 2020; Li et al., 2020), and contrastive loss (Cao and Wang, 2021), and two datasets: CNN/DailyMail (CNNDM, Hermann et al., 2015) and XSum, to fine-tune BART.
We hypothesize that summarization models with low factual consistency are prone to generating summaries less conditioned on the document and perform unconditional summary modeling based on the inductive bias originating from both the training objectives and datasets. To verify our hypothesis, we conduct a shuffle test, in which a summary prefix and an aligned (relevant) or shuffled (irrelevant) document are fed to the model to observe the changes in the model intrinsic features. Specifically, we inspect the conditional summary likelihood, summary prefix saliency, and per decoding step entropy during the shuffle test to determine the document sensitivity of summarization models with respect to the probability, saliency, and entropy, respectively.
For the dataset aspect, we further hypothesize that the (un)conditional summary modeling difficulty (LU and LC in Table 1) of data is one of the causes of the inductive bias related to the factual consistency. We fine-tune GPT-2 (Radford et al., 2019) with summaries of the training set using the NLL, then we measure the NLL of each summary as a proxy for unconditional summary modeling difficulty. Similarly, we fine-tune BART with document/summary pairs of the training set using the NLL, then we measure the NLL of each summary as a proxy for conditional summary modeling difficulty. We split the training samples based on the
(un)conditional summary modeling difficulty and fine-tune BART models using the subset to inspect the relationship between the (un)conditional summary modeling difficulty of the fine-tuning subset and the factual consistency of the fine-tuned BART.
Based on XSum, we empirically show that a summarization model fine-tuned using a subset with high unconditional and low conditional summary modeling difficulty results in an improved factual consistency relative to a subset with low unconditional and high conditional summary modeling difficulty. Table 1 shows an example summary from the models fine-tuned using different training subsets as split by the (un)conditional training loss. We observe that summaries from models fine-tuned with low LU or LU −LC are factually inconsistent containing errors or unrelated information.
Our findings can be summarized as follows:
• Both the UL and CL based models output decreased summary likelihood and summary prefix saliency given the shuffled document compared to NLL based models.
• BART fine-tuned with XSum is less affected by the information in the documents and tends to unconditionally model the summary.
• In XSum, summaries have a distribution that is easily fine-tuned compared to CNNDM, and samples with low unconditional summary modeling difficulty provide an inductive bias that degrades the factual consistency.
We separate the experiments on the model intrinsic features into those for fine-tuning objectives (Section 5.1) and fine-tuning datasets (Section 5.2) to validate that our methods can be used to diagnose the factual consistency of summarization models. In Section 6, we empirically show that both hallucinations in datasets and unconditional training loss cause an inductive bias that the affects factual consistency.

heading: 2 Background
text: We use BART for all experiments and assume that the summarization model consists of a bidirectional encoder and unidirectional decoder, and that the fine-tuned BART follows an autoregressive decoding scheme. At decoding step t, the summarization model generates a t-th token ŷt conditioned to document D = {d1, d2, ..., dM} where the length is M and the previously decoded sequence ŷ<t.

heading: 2.1 Fine-tuning Objectives for Factual Consistency
text: The negative log-likelihood, which is the baseline objective for our experiments, aims to maximize the probability of the reference summary. NLL loss function is defined as follows:
LNLL = − 1
N
N∑
n=1
log pn(yn|y<n, D), (1)
where Y = {y1, y2, ..., yN} denotes the reference summary where the length is N and pn denotes the probability distribution at position n. The NLL does not explicitly guide the model to discriminate factually consistent summaries from factually inconsistent summaries.
Recent studies have proposed training objectives such as UL and CL that exploit well-designed, factually inconsistent summaries (negative samples) during training (Cao and Wang, 2021; Wan and Bansal, 2022). The unlikelihood is augmented with NLL to minimize the likelihood of a negative sample Y ′ = {y′1, y′2, ..., y′N} as follows:
LUL = − 1
N
N∑
n=1
log(1− pn(y′n|y′<n, D)). (2)
The contrastive loss in summarization maximizes the similarity between factually consistent and semantically equivalent summaries (positive samples) while minimizing the similarity between positive and negative samples.
The performances of UL and CL highly depend on the discrimination difficulty of the negative samples, and there are various positive and negative sample construction techniques (Kryscinski et al., 2020; Zhang et al., 2021; Cao and Wang, 2021; Wan and Bansal, 2022; Liu et al., 2022).

heading: 2.2 Summarization Datasets
text: We use CNNDM and XSum for the fine-tuning datasets because their characteristics are different. A reference summary in XSum is designed to be more abstractive than a summary in CNNDM. Consequently, multiple studies have shown that models fine-tuned using XSum generate factually inconsistent summaries more frequently than those finetuned using CNNDM (Maynez et al., 2020; Xie et al., 2021). Recent studies have shown the presence of hallucinated words (i.e. the words that cannot be fully inferred from the given document) in reference summaries lead the model to be factually inconsistent (Lin et al., 2022; Wan and Bansal, 2022).
We also use the Newsroom (Grusky et al., 2018) dataset to compare two BART models fine-tuned using CNNDM and XSum. The reference summary of Newsroom is annotated with the extractiveness according to the degree to which words or phrases are included in the document. By utilizing Newsroom, we intend to compare the two models fixing the extractiveness and length distribution of the evaluation data as much as possible. We use samples labeled with Extractive and Abstractive (samples with Mixed are excluded) for further analyses.

heading: 3 Methodology
text: We analyze the changes in the model intrinsic features when a document and its summary are factually consistent. We control the factual consistency with document-level perturbation by providing an aligned or shuffled document-summary pairs to the model.
We choose document-level perturbation to (i) maximize the difference in the model intrinsic features and (ii) examine whether the model tends to
predict a summary conditioned on the document even if the document is irrelevant. In this section, we explain the intrinsic features of the model.

heading: 3.1 Conditional Summary Likelihood
text: Inspired by Xie et al. (2021), we investigate how the (ir)relevant document affects the conditional likelihood of the summary. Given an aligned or shuffled document D and summary y, we define the conditional summary likelihood (CSL) as follows:
CSL = 1 N
N∑
n=1
pn(yn|y<n, D). (3)
By calculating the difference in the CSL between the aligned and shuffled cases, we aim to measure the document-sensitiveness of the summarization models when relevant or irrelevant documents are given, respectively.

heading: 3.2 Summary Prefix Saliency
text: We quantify the summary prefix saliency (SPS) to observe whether the model focuses on the document to decode the rest of the summary when the document is irrelevant to the given summary prefix.
The gradient-based input saliency (Li et al., 2016; Sundararajan et al., 2017; Arrieta et al., 2020; Atanasova et al., 2020) is used to explain the prediction in the input space. We aggregate the element-wise multiplication of the input embedding and its gradient using the L2-norm, according to Atanasova et al. (2020).
We first calculate the cross-entropy loss L from the summarization model φ and then derive the gradient with respect to each input embedding. Notably, the loss is calculated using a teacher forcing scheme. We define the saliency of the input token x as follows:
Saliencyx = ||e(x)⊙∇e(x)Lφ(D,Y )||2, (4)
where e(x) is the embedding vector of the token x and ⊙ is the element-wise vector multiplication operator.
Based on the input saliency, we calculate the SPS, the input saliency ratio of the summary prefix in the concatenated document and summary, as follows:
SPS = ∑
y∈Y Saliencyy∑ x∈D∪Y Saliencyx . (5)
For each target summary token yn, the SPS quantifies the saliency of the summary prefix y<n.

heading: 3.3 Per Decoding Step Entropy
text: Motivated by the study conducted by King et al. (2022) where the entropy of a model is used as a proxy of the uncertainty, we measure the entropy difference between the aligned and shuffled cases. The entropy difference is used to investigate decoding dynamics and approximate the documentsensitiveness, in terms of the uncertainty of the models.
We measure the entropy of the predicted probability distribution at each decoding step n in the teacher forcing scheme as follows:
Entropyn = − |V |∑
i=1
pn(vi) log pn(vi), (6)
where V = {v1, v2, ..., v|V |} is the vocabulary of the model. For brevity, we omit the document (D) and summary prefix (y<n) conditions in Equation 6. When a shuffled document is given, a relatively low entropy at the decoding step n implies that the model attempts to generate a summarization even if the relevant information does not exist.
By utilizing per decoding step entropy, we focus on analyzing the unconditional summary modeling characteristics of BART fine-tuned with XSum and CNN/DailyMail to investigate inductive bias from the fine-tuning datasets.

heading: 4 Experimental Setup
text: We use BART fine-tuned using the NLL objective provided by fairseq1 (Ott et al., 2019). We refer to the github repository of CLIFF2 (Cao and Wang, 2021) for the fine-tuned models using the CL and scripts for UL based fine-tuning. For the negative sample construction methods, we choose SysLowCon for the CL and MaskEnt for the UL as proposed by Cao and Wang (2021).
During the CSL and SPS measurements, we calculate the log-probability or cross-entropy loss only for important words. Motivated by Xie et al. (2021), we filter out tokens of specific categories by applying part-of-speech tagging using spaCy (Honnibal et al., 2020). We select nouns, verbs, numbers, and proper nouns for the measurements which are more likely to contain factual information. In addition, we filter out the first 30% of the tokens during the loss calculation of the SPS to provide a sufficient summary prefix during the likelihood estimation.
1https://github.com/pytorch/fairseq 2https://github.com/ShuyangCao/cliff_summ
Based on the human evaluation and automatic factual consistency assessment results reported by Cao and Wang (2021), we assume that BART, finetuned using CL, UL, and NLL, shows the highest factual consistency in that order, regardless of the fine-tuning datasets.
In the remainder of this paper, we refer to the BART fine-tuned using NLL, UL, and CL as BARTNLL, BART-UL, and BART-CL, respectively. In the same way, we refer to BART fine-tuned using CNNDM and XSum as BART-CNN and BARTXSum, respectively.

heading: 5 Fine-tuned Model Analysis

heading: 5.1 Fine-tuning Objectives: Conditional Summary Likelihood and Summary Prefix Saliency
text: Analysis on CSL The results of the CSL and SPS analyses are shown in Figure 1. For both finetuning objectives and datasets, the CSL of the shuffled documents is lower than that of the aligned documents.
From a fine-tuning objective perspective, BARTUL and BART-CL show lower CSL values than BART-NLL in the shuffled case, and BART-UL shows the lowest CSL values in both the aligned and shuffled cases. The results imply that factual consistency is negatively related to CSL when an irrelevant document is given; however, preserving a high CSL in aligned cases is also needed.
Analysis on SPS In all shuffled cases, the SPS is higher than that in aligned cases, as shown in Figures 1(c) and (d). This is equivalent to the fact that the model can specify the related context, which is the model intrinsic feature positive to the factual consistency. Both the UL and CL regulate the increment of the SPS in the shuffled case from the SPS in the aligned case. We conclude that the flexibility
of the SPS conditioned on the document is a crucial factor in the factual consistency, and that regulation of the SPS in the shuffled case is required for further improvement of the factual consistency.
Comparison between Fine-tuning Datasets We evaluate the CSL of BART-CNN and BARTXSum on the Newsroom dataset to investigate the model intrinsic features caused by the fine-tuning dataset. As shown in Table 3, the CSL differences between the aligned and shuffled cases of BARTXSum are less than those of BART-CNN regardless of the extractiveness of the summary, except for the unlikelihood objective. We hypothesize that BART-XSum is less conditioned on the information in the document, which is the negative model intrinsic feature for factual consistency. We conduct a further comparison (as discussed in Section 5.2) to validate our hypothesis.

heading: 5.2 Fine-tuning Datasets: Per Decoding Step Entropy
text: We further analyze the dataset aspect because the factual consistency of fine-tuned models largely varies with the dataset characteristics. Figure 2 depicts a graph of the per decoding step entropy averaged over the samples in the Newsroom dataset.
We plot the entropies in the first 100 decoding
steps, considering that the maximum summary token length of the XSum test set is approximately 100. Unlike BART-XSum, the average entropy of BART-CNN slightly decreases after 50 decoding steps. One possible explanation is that BART models fine-tuned with longer summaries are able to better predict the next summary tokens. For a quantitative analysis, we calculate the average entropy over the decoding steps 10 to 100. In both the Extractive and Abstractive subsets, BARTCNN shows higher entropy differences between the aligned and shuffled cases, as shown in Figure 2 (b) and (d). Combined with the results in Table 3, we conclude that BART fine-tuned with XSum is less sensitive to a given document.
We hypothesize that the characteristics of the unconditional summary modeling of BART-XSum originate from the inductive bias, which occurs during the fine-tuning phase and degrades the factual consistency of the summarization model. In Section 6, we provide the experimental results regarding the inductive bias of the factual consistency originating from the fine-tuning datasets.
In Figure 3, we visualize the top-5 predictions,
assuming that the shuffled document and summary prefix are provided. We gather the top-5 predictions for all the decoding positions simultaneously by following the teacher forcing scheme. First, we identify the cases where the top-5 predictions are strongly conditioned on the summary prefix rather than on the document (e.g., the second and first top-5 predictions in BART-XSum and BART-CNN in Figure 3, respectively). In BART-XSum, there are some predictions weakly related to the words in the document (e.g., summer and bee). However, the predictions are not factually consistent and are not similar to the other predictions (e.g., bread, tea, coffee, and money). In contrast, the top-5 predictions in BART-CNN are more similar to each other or are factually consistent.

heading: 6 Modeling Difficulty based Data Splitting and Inductive Bias
text: To clarify one of the causes of the unconditional summary modeling property of BART-XSum, we fine-tune the GPT-2 model (Radford et al., 2019) using the summaries in the training sets of CNNDM and XSum, and plot the training loss curve
in Figure 4. We use a pre-trained GPT-2 from HuggingFace3 (Wolf et al., 2020). Based on the results in Figure 4, we conclude that the unconditional language model fine-tuned with the summaries in XSum converges faster than that fine-tuned with CNNDM. This demonstrates our hypothesis in Section 5.2, i.e., that the low unconditional summary modeling difficulty of XSum results in an inductive bias for the unconditional summary modeling property of the fine-tuned summarization model.

heading: 6.1 Definition
text: We use unconditional and conditional training loss as proxies of unconditional and conditional summary modeling difficulty, respectively. We define the unconditional training loss (denoted as LU ) of the summary as the NLL of the language model fine-tuned with the summary given only the summary. We utilize the fine-tuned GPT-2 to calculate the unconditional training loss. Similarly, we define the conditional training loss (denoted as LC) of the summary as the NLL of the fine-tuned summarization model given the document and summary. We utilize the fine-tuned BART to calculate the conditional training loss. Additionally, we denote a difference between the unconditional and conditional training loss (i.e. LU − LC) as L∆.

heading: 6.2 Setup
text: We design experiments to observe the relationship between (un)conditional summary modeling difficulty and model intrinsic features.
We split the training set into two subsets based on three standards (LU , LC , and L∆), resulting in six fine-tuning subsets in total. We expect that the
3https://huggingface.co/gpt2
samples with high LC prevent unconditional summary modeling properties and that those with low LC assist in the conditional summary modeling.
Our goal is to analyze the six summarization models with respect to factual consistency, CSL, and SPS. For the factual consistency evaluation, we leverage the QuestEval (QEval in short) score (Scialom et al., 2021), a question-answering based automated evaluation method.
Some studies have controlled training samples during summarization model training to improve the factual consistency (Kang and Hashimoto, 2020; Wan and Bansal, 2022; Goyal and Durrett, 2021). For instance, Kang and Hashimoto (2020) adaptively eliminate samples with high loss and Goyal and Durrett (2021) propose identifying nonfactual words in a reference summary and modifying the objective function for the factual consistency. Unlike previous works, we fine-tune both conditional and unconditional language models to calculate the difficulty and analyze the inductive bias during fine-tuning.

heading: 6.3 Results
text: Factual Consistency Analysis We measure the QEval scores of BART fine-tuned using the six categories of training subsets. Table 4 presents the means (with a 99% confidence interval) of 8 different seeds.
In XSum, subsets with low LC or high LU achieve higher factual consistency compared to their counterparts. The difference in the QEval scores between the high and low subsets of LU is larger than those of LC , implying that a low LU causes the inductive bias of the factual consistency degradation in BART-XSum. Furthermore, collecting samples with a high L∆ maximizes the factual consistency. Documents with high L∆ vastly decrease the conditional summary modeling difficulty by using the related information in the document; simultaneously, they roughly filter out the hallucinated samples.
In CNNDM, data splitting based on LC and LU affects the factual consistency less, than that in the case of the XSum results. One possible explanation is that the negative inductive bias for the factual consistency from the dataset is less dominant than the inductive bias from other factors, such as the fine-tuning objectives.
Model Intrinsic Feature Analysis We further analyze BART-XSum with regards to the CSL and SPS to determine the correlation between LU and resulting model intrinsic features. In Figure 5, we visualize the CSL and SPS based on LU and L∆, because we assume that LU is the major factor in the unconditional summary modeling characteristics of the models fine-tuned using XSum.
It is observed that the models fine-tuned with XSum subsets of the high group show lower CSL and SPS in the shuffle case than the models of the low group. When comparing LU and L∆, it is observed that the gap in the CSL between the models in the high and low groups is larger in L∆. The regulated CSL and SPS of the models of the high group in the shuffle case indicate that LU is related to the inductive bias of unconditional summary modeling and factual consistency.

heading: 7 Related Work

heading: 7.1 Fine-tuning Methods for Factual Consistency Enhancement
text: Sequence-to-sequence models, widely used in text generation tasks such as language modeling (Devlin et al., 2019; Radford et al., 2019) and summarization (Lewis et al., 2020; Zhang et al., 2020) are commonly trained under the NLL.
Despite their powerful modeling performance, language models trained to optimize the NLL often encounter the text degeneration problem where
the generated texts contain repetitive words or inconsistent contexts (Holtzman et al., 2020). UL was proposed to prevent such problems by penalizing the modeling probability of unwanted tokens (Welleck et al., 2020; Li et al., 2020).
CL was also proposed to enhance factual consistency in the summarization models. Cao and Wang (2021) fine-tune pre-trained language models with a contrastive loss to learn distinguishable representations of the factually erroneous summarization outputs from the sound ones. Wan and Bansal (2022) propose factual consistency enhancing methods with pre-training objectives and finetuning modules.
In this paper, we compare the fine-tuning objectives for factual consistency with respect to the conditional summary likelihood and summary prefix saliency to find the relationship between the fine-tuning objectives and the model intrinsic features.

heading: 7.2 Analysis of Summarization Datasets and Models
text: There have been several studies analyzing the properties of summarization datasets (Kryscinski et al., 2019; Maynez et al., 2020; Bommasani and Cardie, 2020). For instance, Maynez et al. (2020) inspect intrinsic/extrinsic hallucinations in modelgenerated summaries on the XSum dataset, and Bommasani and Cardie (2020) quantify the intrinsic features of datasets, such as their topic similarity and abstractivity. In this paper, we focus on the inductive bias from the datasets and resultant model intrinsic features rather than the characteristics of the dataset itself.
On the other hand, the characteristics of the abstractive summarization model have also been studied (Kang and Hashimoto, 2020; Pagnoni et al.,
2021; Xu et al., 2020; Cao and Wang, 2021; Liu et al., 2022; West et al., 2022). Xu et al. (2020) relate model intrinsic features such as entropy and attention to a model prediction. Liu et al. (2022) and Cao and Wang (2021) analyze the characteristics of the predicted hallucinated words with respect to model intrinsic features such as probability and entropy. West et al. (2022) conduct factual ablation studies to observe the probability differences when essential information is ablated from a document.
In this paper, we analyze the model with respect to the datasets and fine-tuning objectives and focus on the intrinsic features of the fine-tuned model, such as the SPS and unconditional summary modeling property. Unlike West et al. (2022), we provide aligned and shuffled documents to maximize the CSL, SPS, and entropy differences.
Kang and Hashimoto (2020) report that the losses of samples containing hallucinations are higher than those of others. We also exploit the loss during the data splitting experiments, but we additionally utilize the unconditional modeling loss of the summary. Furthermore, we integrate the loss with the inductive bias of the fine-tuned models.

heading: 8 Conclusion
text: In this work, we analyze the model intrinsic features that contribute to factual consistency enhancement of the summarization model. With the assumption that summarization models with low factual consistency tend to ignore information from the given documents, we conduct shuffle tests and propose a unified view of the intrinsic features on the fine-tuning objectives and datasets. We measure the CSL, SPS, and per decoding step entropy of BART models to clarify the model intrinsic features related to factual consistency. We also correlate the unconditional and conditional summary modeling difficulty with the inductive bias for factual consistency through data splitting experiments. Based on the analyses, we anticipate that our method can be used as an indicator of the factual consistency of summarization models.
Limitations
In this paper, we use a pre-trained BART to observe the changes in the model intrinsic features by varying the fine-tuning objectives and datasets. However, our methods can be made significantly more generalizable when the range of summarization models (e.g., PEGASUS (Zhang et al., 2020))
and datasets (e.g., FRANK benchmark (Pagnoni et al., 2021)) are broadened. Additionally, we can try using other evaluation methods, such as FactCC (Kryscinski et al., 2020), reported to have a high correlation with human judgment (Pagnoni et al., 2021) to interpret the model intrinsic behavior during the shuffle tests.
We leave the research on methods to optimize the inductive bias during the fine-tuning process to improve the factual consistency as future work. Our experimental results can also be integrated with those of previous studies focusing on hallucinations in datasets (Kryscinski et al., 2020; Wan and Bansal, 2022).

heading: Acknowledgement
text: The authors would like to thank the researchers at Samsung SDS for their invaluable support. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (2022R1A3B1077720 and 2022R1A5A708390811), Institute of Information & communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) [2022-0-00959 and 2021-0-01343: Artificial Intelligence Graduate School Program (Seoul National University)], Samsung Electronics Co., Ltd (IO221213-04119-01), SNU-Naver Hyperscale AI Center, and the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023.
Ethics Statement
Our goal is to diagnose factual consistency of finetuning based summarization models and prevent models from providing human with misleading information. By connecting model intrinsic features and factual consistency, we aim to improve explainability and faithfulness of summarization models. One of major concerns is that there are still aspects of the model which are not explored, such as scaling law of factual consistency. Continuous studies on model intrinsic features would result trustful summarization models which we leave as future work.

heading: A Fine-tuning Language Model for Dataset Split
text: To split the fine-tuning datasets based on the modeling difficulties, we fine-tune the pre-trained GPT2-base model (Radford et al., 2019) from HuggingFace4 (Wolf et al., 2020) using the reference summary set from each dataset. We re-train the bytepair encoding tokenizer of the GPT-2 model with XSum (or CNN/DailyMail) subset for better adaptation on the summarization domain. The final language models using CNN/DailyMail and XSum are fine-tuned for 12,000 and 9,000 steps, respectively, using early stopping with the validation loss. We split the fine-tuning dataset to a block size of 1024, use a batch size of 32, and set an initial learning rate of 5e-5.

heading: B Decoding Details
text: We follow the same decoding scheme, i.e., beam search decoding, as Cao and Wang (2021). We also use the same hyperparameters of decoding: fairseq5 (Ott et al., 2019) and CLIFF6 (Cao and Wang, 2021).
For CNN/DailyMail, we set the beam width to 4 and the minimum length to 55. For XSum, we set the beam width to 6 and the minimum length to 10.
4https://huggingface.co/gpt2 5https://github.com/facebookresearch/fairseq/
blob/main/examples/bart/summarize.py 6https://github.com/ShuyangCao/cliff_summ/ tree/main/scripts/bart

heading: C Dataset Statistics
text: Statistics of XSum, CNN/DailyMail, and Newsroom are shown in Table 5. Note that we only use the test set of Newsroom because we use Newsroom during the evaluation of BART-CNN and BART-XSum.

heading: D Model Size and Training Time
text: We fine-tune BART-large which consists of 400M parameters for the experiments. To calculate unconditional training loss of summary, we fine-tune GPT-2 which consists of 117M parameters. Detailed fine-tuning times for the models are shown in Table 6.

heading: E License of Repositories and Datasets
text: The repository of fairseq is under the MIT license, and the repository of huggingface is under the Apache-2.0 license. The repositories of CNN/DailyMail and Newsroom are under the Apache-2.0 license, and the repository of XSum is under the MIT license.

heading: F Conditional Summary Likelihood on PEGASUS
text: We compare the CSL of fine-tuned PEGASUS (Zhang et al., 2020) with respect to fine-tuning objective following hyperparameters as Cao and Wang (2021). Table 7 shows results similar to BART: in the shuffle case, PEGASUS fine-tuned using the CL shows less CSL compared to its NLL counterpart.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: "Limitations", just after "8 Conclusion" (without section number)
7 A2. Did you discuss any potential risks of your work? Analyzing and enhancing factual consistency of text summarization will reduce potential risk of fake news or false information.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: "Abstract" and "1 Introduction"
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? "5 Fine-tuned Model Analysis", "6 Modeling Difficulty based Data Splitting and Inductive Bias"

heading: 3 B1. Did you cite the creators of artifacts you used?
text: "2.2 Summarization Datasets", "4 Experimental Setup", "5 Fine-tuned Model Analysis", "6 Modeling Difficulty based Data Splitting and Inductive Bias"

heading: 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: "Appendix E License of Repositories and Datasets"
7 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided 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)? Provider of used artifacts did not provide intended use.
7 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Provider of used artifacts did not provide such steps.
7 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? We provide examples of datasets which indirectly provide domain and language information.
3 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. "Appendix C Dataset Statistics"
C 3 Did you run computational experiments? "5 Fine-tuned Model Analysis", "6 Modeling Difficulty based Data Splitting and Inductive Bias"

heading: 3 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? "Appendix A Fine-tuning Language Model for Dataset Split", "Appendix D Model Size and Training Time"
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
7 C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? We follow hyperparameters of previous work. For GPT-2 fine-tuning, we did not conduct hyperparameter search because we observe that training/validation loss is converged , and we use GPT-2 for the comparison rather than the absolute value evaluation.

heading: 3 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? "6.3 Results"
7 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.)? We use default settings and hyperparameters of Spacy and QuestEval.
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? No response.
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 crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: , Ltd (IO221213-04119-01), SNU-Naver Hyperscale AI Center, and the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023