Annotating and Modeling Fine-grained Factuality in Summarization (2021)

Abstract: Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors. While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing. We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. Our observations are threefold. First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSum. Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data. Finally, we show that our best factuality detection model enables training of more factual XSum summarization models by allowing us to identify non-factual tokens in the training data.

## introduction hallucination of unsupported or incorrect facts is a known shortcoming of current text generation and summarization models @xcite . this has been established for both abstractive summarization models @xcite and extractive summarization models @xcite . past work has explored using off-the-shelf frameworks such as entailment models @xcite or qa systems @xcite to detect and sometimes correct errors in generated summaries. another line of recent work has used synthetically generated data to specifically train models on the factuality detection task @xcite @xcite . however, these efforts have focused on different datasets, summarization systems, and error types, often shedding little light on what errors state-of-the-art systems are actually making and how to fix them. in this paper, we aim to answer two main questions. first, while synthetic data generation approaches are specifically designed for factuality evaluation, do these align with actual errors made by generation models? we find the answer is no: techniques using surface-level data corruption @xcite @xcite or paraphrasing @xcite ) target inherently different error distributions than those seen in actual model generations, and factuality models trained on these datasets perform poorly in practice. furthermore, we show that different summarization domains, cnn/daily mail @xcite and xsum @xcite (which differ in the style of summaries and degree of abstraction), exhibit substantially different error distributions in generated summaries, and the same dataset creation approach cannot be used across the board. second, we investigate the best approach for modeling and learning factuality, particularly for highly abstractive summarization settings @xcite . specifically, we compare the utility of fine-grained human annotations (such as error highlighting at the word-or span-level) with sentence-level factuality annotations. we use a prior factuality detection model capable of leveraging such fine-grained annotations @xcite and show that these allow us to more reliably detect errors as well as localize those errors within generated texts. in fact, fine-grained reference summary: an early-medieval gold pendant created from an imitation of a byzantine coin that was found in a norfolk field is a "rare find", a museum expert has said. source article fragment: discovered on land at north elmham, near dereham, the circa 600 ad coin was created by french rulers of the time to increase their available currency. […] the pendant was declared treasure by the norfolk coroner on wednesday. an 18th century coin believed to be worth more than #1m has been discovered. ## training datasets to compare we first seek to answer how well synthetic training data can help address factuality errors observed in real summarization datasets. figure 1 shows a summary of the approaches we consider, which we describe in detail in section 2.1 and 2.2. the summarization models we analyse are trained on two english-language domains: (1) xsum, an "extreme" summarization dataset from british broadcasting corporation (bbc) articles, where the first sentence of the article is treated as a summary of the article. these summaries are highly abstractive in nature: summarization models trained on this dataset have to learn to model long-range dependencies and may still be unable to recover all information in the gold summary. for both of these domains, we compare the distribution of factuality errors from synthetic training data with the distribution of observed factuality errors from models trained on that data. in section 4, we further dive into factuality models' performance in these settings. ## analysis of error types past work using synthetic training data implicitly assumes that training a factuality model on such data will allow it to transfer to realistic settings. we start by qualitatively analyzing the actual errors produced by summarization models to see how these align with the synthetic data, which helps us better understand this assumption. we identify four broad categories of errors (see figure 3 ) that we will identify through manual inspection. each of these categories is further divided into intrinsic (errors that arise as a result of misinterpreting information from the source article) and extrinsic (errors that hallucinate new information or facts not present in the source article), following the characterization from @xcite . our taxonomy of summarization errors differs from that of @xcite : theirs is targeted at the effects on the reader, whereas ours is more directly tied to the grammatical role of the error, which we believe is more useful to improve our data and our systems. we use the above taxonomy to annotate examples from both summarization domains. for xsum, we use the state-of-theart bart model @xcite to generate summaries followed by manual annotation (100 examples). for cnn/dm, annotation was done on the 50 summaries across 10 different models collected by @xcite . we additionally do this annotation for the artificially introduced errors in ent-c and gen-c. 2 results figure 4 shows the distribution of errors for these different settings. first, we see that summarization models from different domains make substantially different types of errors. models trained on xsum learn to hallucinate new content and consequently produce extrinsic errors: 60% of the errors made by bart models are extrinsic. one reason for this is that the xsum data was automatically constructed and contains gold summaries that are noisy or non-factual (75% of gold summaries, according to @xcite ). in addition to this, the gold summaries are also highly abstractive, and xsum-trained summarization models learn to combine informa- seven games involving nimes were arrested. nsubj:pass seven games involving nimes were investigated. [sep] electra d(s) e(d; s) r a y @xmath0 figure 5 : the dependency arc entailment (dae) model from @xcite . a pre-trained encoder is used to obtain arc representations; these are used to predict arc-level factuality decisions. ## localization of errors our evaluation so far has focused on the sentencelevel performance of factuality models. next, we evaluate the models' ability to localize errors within the generated summary as well as show how such a capability can be leveraged to train less error-prone summarization models. ## related work earlier work on abstraction @xcite and compression @xcite @xcite in summarization has typically focused evaluation on content selection and grammaticality, with little heed paid to factuality. human evaluation similarly focused on content selection @xcite . methods such as pyramid @xcite ) that could have in principle been used to evaluate factuality were primarily used to understand content selection. recent work has explored different methods for enforcing factuality: modifying the model, such as encoding srl structures in the input @xcite , post-hoc correction @xcite , or constrained decoding @xcite . however, these techniques fundamentally struggle to handle the whole range of factual errors; factuality is a fuzzy notion and cannot be easily encapsulated into a set of discrete rules. faithfulness and factuality have also been tackled in related tasks, including summarizing radiology reports @xcite and data-to-text generation tasks @xcite . another recent line of work has looked at fact verification @xcite @xcite . in this literature, the claims are usually humanauthored and a straightforward statement of a fact, whereas generated summaries might feature claims buried in nominal modifiers like two-time winner. ## conclusion in this work, we showed that existing synthetic datasets are not well-suited to factuality evaluation of recent summarization models (like bart) in challenging domain (like xsum). models trained on human-annotated data, especially those that leverage fine-grained annotations, can enable training of more factual summarization models. we hope future work will explore better modeling and data creation to address the pressing issues in current systems.