Evaluating the Factual Consistency of Abstractive Text Summarization (2020)

Abstract: The most common metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and generated summaries. Training data is generated by applying a series of rule-based transformations to the sentences of source documents. The factual consistency model is then trained jointly for three tasks: 1) predict whether each summary sentence is factually consistent or not, 2) in either case, extract a span in the source document to support this consistency prediction, 3) for each summary sentence that is deemed inconsistent, extract the inconsistent span from it. Transferring this model to summaries generated by several neural models reveals that this highly scalable approach outperforms previous models, including those trained with strong supervision using datasets from related domains, such as natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency. We also release a manually annotated dataset for factual consistency verification, code for training data generation, and trained model weights at https://github.com/salesforce/factCC .

## introduction the goal of text summarization is to transduce long documents into a shorter form that retains the most important aspects from the source document. common approaches to summarization are extractive @xcite where models directly copy salient parts of the source document into the summary, abstractive @xcite where the important parts are paraphrased to form novel sentences, and hybrid @xcite , combining the two methods by employing specialized extractive and abstractive components. advancements in neural architectures @xcite @xcite , transfer learning @xcite , and availability of large-scale supervised datasets @xcite allowed deep learning-based approaches to dominate the field. state-of-the-art solutions utilize self-attentive transformer blocks @xcite , attention and copying mechanisms @xcite , and multi-objective training strategies @xcite , including reinforcement learning techniques @xcite @xcite . despite significant efforts made by the research community, there are still many challenges limiting progress in summarization: insufficient evaluation protocols that omit important dimensions, such as factual consistency, noisy datasets that leave the task underconstrained, and strong, domain-specific layout biases in the data that dominate training signal @xcite . we address the problem of verifying factual consistency between source documents and generated summaries: a factually consistent summary contains only statements that are entailed by the source document. recent studies show that up to 30% of summaries generated by abstractive models contain factual inconsistencies @xcite @xcite . such high levels of factual inconsistency render automatically generated summaries virtually useless in practice. the problem of factual consistency is closely related to natural language inference (nli) and fact source article fragments (cnn) the mother of a quadriplegic man who police say was left in the woods for days cannot be extradited to face charges in philadelphia until she completes an unspecified "treatment," maryland police said monday. the montgomery county (maryland) department of police took nyia parler, 41, into custody sunday (...) (cnn) the classic video game "space invaders" was developed in japan back in the late 1970's -and now their real-life counterparts are the topic of an earnest political discussion in japan's corridors of power. luckily, japanese can sleep soundly in their beds tonight as the government's top military official earnestly revealed that (...) ## related work this work builds on prior research in factual consistency in text summarization and natural language generation. @xcite proposed an automatic, model-dependent metric for evaluating the factual accuracy of generated text. facts are represented as subject-relation-object triplets and factual accuracy is defined as the pre-cision between facts extracted from the summary and source document. despite positive results, the authors highlighted remaining challenges, such as its inability to adapt to negated relations or relation names expressed by synonyms. a parallel line of research focused on improving factual consistency of summarization models by exploring different architectural choices and strategies for both training and inference. in @xcite , the authors proposed re-ranking potential summaries based on factual correctness during beam search. the solution used textual entailment (nli) models to score summaries by means of the entailment probability between all source document-summary sentence pairs. the summary with the highest aggregate entailment score was used as the final output of the summarization model. the authors concluded that out-of-the-box nli models do not transfer well to the task of factual correctness. in @xcite , the authors proposed a novel, dual-encoder architecture that in parallel encodes the source documents and all the facts contained in them. during generation, the decoder attends to both the encoded source and facts which, according to the authors, forces the output to be conditioned on the both inputs. human evaluation showed that the proposed technique substantially lowered the number of errors in generated single-sentence summaries. the synthetic data generation process proposed as part of our approach is based on prior work done in the domains of data augmentation and weakly-supervised learning. @xcite proposed an augmentation framework aimed at boosting performance of text classification models. the authors used 4 text transformations to synthesize data: synonym replacement, random insertion, random swap, random deletion, and showed increased performance of classifiers on 5 downstream tasks, both for convolutional and recurrent neural models. in @xcite the authors introduced and analyzed the effects of using backtranslation based data augmentation on the performance of machine translation models, while @xcite used the mentioned transformation to synthesize training data for a paraphrase generation network. @xcite investigated a two step approach for training text classification models on weakly-supervised data, which includes pretraining models on fully synthetic data. ## methods a careful study of the outputs of state-of-the-art summarization models provided us with valuable insights about the specifics of factual errors made during generation and possible means of detecting them. primarily, checking factual consistency on a sentence-sentence level, where each sentence of the summary is verified against each sentence from the source document, is insufficient. some cases might require a longer, multi-sentence context from the source document due to ambiguities present in either of the compared sentences. summary sentences might paraphrase multiple fragments of the source document, while source document sentences might use certain linguistic constructs, such as coreference, which bind different parts of the document together. in addition, errors made by summarization models are most often related to the use of incorrect entity names, numbers, and pronouns. other errors such as negations and common sense error occur less often. taking these insights into account, we propose and test a document-sentence approach for factual consistency checking, where each sentence of the summary is verified against the entire body of the source document. ## analysis to further understand performance of our proposed models, we conducted human-based experiments and manually inspected model outputs. ## conclusions we introduced a novel approach for factual consistency checking of summaries generated by abstractive neural models. in our approach, models are trained to perform factual consistency checking on the document-sentence level, which allows them to handle a broader range of errors in comparison to previously proposed sentencesentence approaches. models are trained using artificially generated, weakly-supervised data created based on insights coming from the analysis of errors made by state-of-the-art summarization models. quantitative studies showed that on less abstractive domains, such as cnn/dailymail news articles, our proposed approach outperforms other models trained on existing textual entailment and fact-checking data, motivating our use of weak-supervision over transfer learning from related domains. experiments with human annotators showed that our proposed approach, including an explainable factual consistency checking model, can be a valuable tool for assisting humans in factual consistency checking. shortcomings of our approach explained in section 5.2 can serve as guidelines for future work. we hope that this work will encourage continued research into factual consistency checking of abstractive summarization models.