Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency (2023)

Abstract: In this study, we analyze the model intrinsic features of a summarization model by varying the fine-tuning objectives and datasets. We fine-tune 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.

## introduction 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 @xcite @xcite . recently, the fine-tuning of pre-trained language models has resulted in an excellent performance with improved factual consistency @xcite @xcite . 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 @xcite @xcite . they document ludosport has opened its first academy teaching seven forms of combat from the star wars world using flexible blades mounted on weighted hilts. the sport began eight years ago in italy but has only just come to england with the first classes in cheltenham. instructor jordan court said people were already "hooked". ... so far there are six pupils, but this number is expected to increase. ... the sport is so new to england that there have only been a handful of classes so far ... lightsaber combat academy there are several ranks for those wishing to become a fully-fledged jedi knight: summary high lu -lc : a lightsaber-wielding martial artist has opened an academy in cheltenham to teach people how to "fight like a jedi". high lu : a new martial art inspired by the star wars franchise has come to the uk for the first time. low lu : hundreds of people are taking part in the uk's first lightsaber combat class. low lu -lc : if you're a fan of star wars, you might want to think twice before taking up lightsaber combat. factual consistency also depends on the finetuning datasets. in particular, models trained with xsum @xcite , where the reference summaries are highly abstractive, are known to show degenerated factual consistency @xcite @xcite . there have been several studies on the properties of datasets in this context @xcite @xcite @xcite ; @xcite . for instance, @xcite and @xcite report that models trained using xsum are more likely to generate hallucinated words, which is critical to the factual consistency. @xcite inspect the dis-tribution of predicted hallucinations in terms of the probability and entropy of model predictions. @xcite 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 @xcite , 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, @xcite , and contrastive loss @xcite , and two datasets: cnn/dailymail @xcite 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 (l u and l c in 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. our findings can be summarized as follows: 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. ## background 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 @xmath0, d 2 , ..., d m } where the length is m and the previously decoded sequence ŷ<t . ## methodology 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. ## experimental setup we use bart fine-tuned using the nll objective provided by fairseq foot_0 @xcite . we refer to the github repository of cliff foot_1 @xcite 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 @xcite . during the csl and sps measurements, we calculate the log-probability or cross-entropy loss only for important words. motivated by @xcite , we filter out tokens of specific categories by applying part-of-speech tagging using spacy @xcite . 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. based on the human evaluation and automatic factual consistency assessment results reported by @xcite , 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 bart-nll, bart-ul, and bart-cl, respectively. in the same way, we refer to bart fine-tuned using cnndm and xsum as bart-cnn and bart-xsum, respectively. ## modeling difficulty based data splitting and inductive bias to clarify one of the causes of the unconditional summary modeling property of bart-xsum, we fine-tune the gpt-2 model @xcite using the summaries in the training sets of cn-ndm and xsum, and plot the training loss curve 1k 2k 3k 4k 5k 6k 7k 8k 9k training step 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 gpt-2-cnn gpt-2-xsum in figure 4 . we use a pre-trained gpt-2 from hug-gingface 3 @xcite . 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. ## conclusion 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.