Self-Repetition in Abstractive Neural Summarizers (2022)

Abstract: We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of n-grams of length four or longer that appear in multiple outputs of the same system. We analyze the behavior of three popular architectures (BART, T5, and Pegasus), fine-tuned on five datasets. In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition. Fine-tuning on more abstractive data, and on data featuring formulaic language is associated with a higher rate of self-repetition. In qualitative analysis, we find systems produce artefacts such as ads and disclaimers unrelated to the content being summarized, as well as formulaic phrases common in the fine-tuning domain. Our approach to corpus-level analysis of self-repetition may help practitioners clean up training data for summarizers and ultimately support methods for minimizing the amount of self-repetition.

## introduction sequence-to-sequence neural models for conditional text generation such as bart @xcite , t5 @xcite , and pegasus @xcite achieve strong empirical results on abstractive summarization tasks. the summaries that such systems output often appear to be novel, in that they repeat text verbatim from inputs sparingly or not at all. here, we set out to study the novelty of models with respect to their own outputs, by measuring the extent to which the content a model generates is formulaic repetition produced across inputs. more specifically, we analyze how often long n-grams (@xmath0. repetition of some such n-grams may be natural, for example in news covering the same type of event, or in academic papers with accepted formulaic descriptions of research questions and findings. to contextualize our measurements, we therefore contrast repetition in summaries written by humans with what we observe in system outputs. the former provides a baseline expectation regarding how much repetition is normal in a particular domain. in three out of the five domains we study we find that long n-gram repetition is considerably higher in automatically produced summaries than in human-written summaries. in the fourth domain, scientific papers, selfrepetition even in human summaries is so high that the measure we use may not be sensitive enough to distinguish differences in repetition at this range. we hypothesized that such undesirable behavior can be easier to quantify when we evaluate systems across domains, tasking a system trained in one domain to generate summaries in another. the intuition was that the repeated n-grams will be typical for the fine-tuning domain but rare in the test domain, so problematic repetitions may be easier to detect. this setting leads to clear cases of hallucinations reflecting the training data, e.g., fine-tuning bart @xcite on an academic paper summarization dataset and then applying it to a news summarization task yields hundreds of generated summaries that contain the phrase this paper reports the results of an investigation. further, the phrase the past few years have seen a dramatic increase appears in a dozen news summaries, as do slight variations. to characterize this repetition behavior quantitatively, we perform a regression analysis in which we include as predictors system architecture, as well as training and test datasets (section 6). we find that bart @xcite is especially repeating n-gram freq click here for all the latest transfer news 73/11490 example: moha el ouriachi is set to sign for stoke city, according to his agent. the 19-year-old barcelona b player is keen to seek first-team action. stoke have already signed bojan krkic and marc muniesa from barcelona. click here for all the latest transfer news. this paper reports the results of an investigation 143/11490 example: schoolgirl killer zbigniew huminski was arrested for a range of crimes which are likely to see him jailed for life . this paper reports the results of an investigation into the circumstances under which he was arrested in the northern port city of calais in our series of letters from african-american journalists, film-maker and columnist farai sevenzo considers 16/11490 example: in our series of letters from african-american journalists, film-maker and columnist farai sevenzo considers the lessons learned from the 2013 boston marathon bombings. however, there is insufficient evidence to 1086/6440 example: @xmath3 is an effective solution for the vacuum state of qcd . however, there is insufficient evidence to support or refute the use of lattice simulations with @xmath3. but there is a lack of evidence to support 103/6440 example: the apple watch is officially going on sale -but there is a lack of evidence to support its decision to make it available through online orders. ## related work prior work in abstractive neural summarization has focused on phrases repeated within a given output, and proposed various means for mitigating this problem @xcite @xcite . by contrast, our work quantifies the extent to which systems produce the same n-grams across different inputs, and the factors that correlate with this behavior. research in text generation has documented that systems often self-repeat and have quantified how much models repeat content from their pre-training data @xcite . we provide some puzzling examples where we are unable to trace the origin of repeated content foot_0 . we also recognize a portion of the repetitions as hallucinations that are influenced by the training data. oftentimes, the hallucinations are stylistic, similar to the formulaic phrases from academic papers that we mentioned in the introduction. prior work has shown that neural summarization systems are capable of choosing important content across domains but need in-domain data to faithfully reproduce the style of a given domain @xcite . in our work, we find that once systems pick up stylistic templates from one domain, they are likely to reuse them in other domains, where the formulaic phrases look out of place. self-repetition is well-documented in dialog systems research. dialog systems often produce generic formulaic responses regardless of the preceding utterance @xcite : in one of the reported experiments, four generic responses (i don't know, i don't know what you are talking about, i don't think this is a good idea, oh my god) constitute 32% of system generated responses. these phrases were common in the training data, with 0.4% of training data sentences containing the phrase i don't know, even though overall the training data was diverse. our findings for summarization are similar, as we discover in our regression analysis that training on data with higher incidence of formulaic phrases, like academic papers and summaries of medical evidence, results in a summarizer that is overall more likely to repeat content across inputs, at rates markedly higher than done by humans. human summaries are typically considered an appropriate reference while enhancing abstractive text summarization models @xcite . for our analysis too, we contrast model generated summaries against the human summaries as baseline to determine the threshold over which self-repetition is considered anomalous. ## defining self-repetition we introduce a repetition score to measure how often systems repeat themselves. the score is a function of n-grams of length four and longer in different summaries, which is indicative of text similarity and potential pliagiarism @xcite . we consider an n-gram to be repeating when it appears in two or more summaries in a dataset. the repetition score can be computed at the dataset and individual summary level. at the dataset level, we count the number of summaries that contain at least one n-gram (@xmath1. we define the repetition score for a dataset as the number of summaries containing repeating n-grams divided by the total number of summaries in that dataset. we divide by the total in order to normalize the values allowing for meaningful comparison between datasets of different sizes. for an individual summary, we define the repetition score as: where i indexes summaries, m is the number of repeating n-grams in summary i, and n k denotes the count of summaries that contain the kth repeating n-gram found within summary i. we take the log to this value to produce the final score, to make the repetition score less sensititive to outliers. ## models and datasets we consider three models: bart @xcite , t5 @xcite , each fine-tuned on five summarization datasets: cnn/dailymail @xcite , bbc xsum @xcite , scientific papers (sp; @xcite , reddit @xcite and a corpus of randomized controlled trials (rcts; @xcite . we evaluate each model on the five datasets, yielding 75 (3•5•5) combinations of architectures, train, and test datasets. mail and xsum-is somewhere in-between: 60-70% of human summaries contain a long repeated n-gram. in model outputs we observe a level of repetition similar to what is seen in the references on the reddit and scientific papers dataset. for news corpora (cnn and xsum) and the medical evidence summarization task (rcts) however, system repetition scores are markedly higher than the scores for the human-written summaries. bart seems particularly prone to repetition. we contrast the repetition score of the human summaries in each domain with their level of abstractiveness, defined as the fraction of n-grams of a given size that do not appear in the input (and so are "novel"). as pointed out in @xcite , reference summaries in xsum are more abstractive than those in the cnn/daily mail dataset. ## qualitative analysis to glean a qualitative view of repetition behavior, we randomly sampled 20 long n-grams that appeared in more than 10 summaries. these n-grams often do not appear in the corresponding inputs. we show examples in summary: in our series of letters from african journalists, filmmaker and columnist ahmedou ould-abdallah reflects on his time at the university of cape town. input :this is great. i hope you discuss some of these points in the next episode of tldr: clg. summary: in our series of letters from african journalists, filmmaker and columnist gustavom looks at the relationship between humans and animals. input: listen to gustavom, a friendship could never come close to that of a companion.tl;dr: gustavom is right. summary: in our series of letters from african journalists, filmmaker and columnist ahmedou ould-abdallah reflects on his time at the university of cape town. input: we had to take business writing classes to graduate in finance. it was essentially a class on how to do effective tl;drs. ## regression analysis we next quantify the association between selfrepetition and factors that might influence this, including system architecture and pre-training, and the datasets used for training and testing. we would also expect that repetition would be proportional to summary length: more words naturally afford more opportunities for repetition, even if by chance. and indeed we observe that the repetition scores of human summaries are proportional to their average lengths. we report summary lengths in appendix table a1 which can be compared to the repetition scores in we fit a regression model to 731,406 summaries generated by 75 combinations of architecture, train and test data, along with the reference summaries for all datasets. we have multiple one-hot encoded categorical variables, which means we must select reference categories for these (effectively the intercept term). we use human generated summaries as the reference architecture and the cnn/daily mail as the reference train and test sets. this model treats the repetition observed in a given summary as defined in equation 1 as a lin-ear function of predictors including: the length of the generated summary in number of white space delimited tokens (length of summary); the model architecture used to generate the summary. differences in pre-training data will be folded in the behavior due to architecture (bart, t5, pegasus); the training data to which this model was fit; the test data for which a summary is produced; and interaction terms between train and test datasets. the latter we denote by "train -test", e.g., "xsum -reddit" indicates a summary produced by a model fine-tuned on xsum given an input drawn from the reddit corpus. this is a cross-domain model. by contrast, "xsum -xsum" is an in-domain example of a summary produced on an xsum test instance by a model fine-tuned using the xsum training data. in to ascertain whether domain shift (in general) is indeed a significant factor associated with repetition, we perform a likelihood ratio test with the interaction terms. specifically we use as our nested model a regression with all interaction terms omitted, and compare this to the full model with all factors. we choose 0.001 as the critical value. the likelihood ratio test results in a p-value @xmath2.001. this implies that the domain interactions do impart information in terms of quantifying the self-repetition, i.e., applying a summarization model to data from a domain that differs from its training source correlates with increased repetition. ## conclusions we evaluated the tendency of neural summarization models to repeat themselves across outputs on five datasets. to our knowledge this is the first analysis of this phenomenon. our results indicate that bart has the greatest tendency to self-repeat, and that the training source is a significant factor which may lead to this repetition behavior. adapting a summarization model trained on one domain to another (distinct) domain also correlates significantly with repetition; the model may "not know what to say" in such cases, and default to stock phrases from the training data. we also found that models sometimes repeat long strings of text that do not contain any references in the corresponding inputs or even the training sets. these may originate in pre-training data, but more research into such hallucinations is warranted. we hope this analysis will encourage development of methods for mitigating the repetition across summaries and for controlling hallucinations in abstractive neural summarizers. jingqing zhang, yao zhao, mohammad saleh, and peter liu. 2020. pegasus: pre-training with extracted gap-sentences for abstractive summarization. in international conference on machine learning, pages 11328-11339. pmlr.