Automatic summarization aims to produce summaries that are succinct, coherent, relevant, and — crucially — factually correct. Recent progress in conditional text generation has led to models that can generate fluent, topical summaries BIBREF2. However, model-generated summaries frequently contain factual inconsistencies, limiting their applicability BIBREF3.The problem of factual inconsistency is due in part to the lack of automatic evaluation metrics that can detect such errors. Standard metrics for evaluating generated text are predominantly based on counting $n$-grams, which weigh all $n$-grams equally and are insensitive to semantic errors. This inadequacy leaves human evaluation as the primary method for evaluating the factual consistencies, which has been noted to be challenging even for humans BIBREF4, BIBREF5, in addition to being slow and costly.We argue that evaluation metrics that are able to capture subtle semantic errors are required to build better models. In this work, we introduce a general framework for evaluating conditional text generation that is designed to detect factual inconsistencies in generated text with respect to some input. Our framework consists of three steps: (1) Given a generated text, a question generation (QG) model generates a set of questions about the text. (2) We then use question answering (QA) models to answer these questions given both the input and the generated text. (3) A quality score is computed based on the similarity of corresponding answers.This approach leverages recent progress in QA and QG to ask and answer human readable, on-topic questions BIBREF6, BIBREF7. It only assumes access to a question answering dataset to train the QG and QA models, and is applicable to any modality where a QA model is available, e.g. text, images, or knowledge graphs.We use this framework to develop QAGS (Question Answering and Generation for Summarization), a metric for evaluating the factual consistency of abstractive document summaries. Compared to commonly used automatic metrics such as ROUGE BIBREF8, QAGS shows dramatically higher correlations with human judgements of factuality, for example achieving a Pearson correlation coefficient of 54.52 on the CNN/DailyMail summarization task, compared to 17.72 for ROUGE-2. QAGS also achieves new state-of-the-art results on evaluating the factuality of summaries, outperforming recently proposed NLI models for this task BIBREF5.Finally, we analyse the robustness of QAGS through an ablation study. QAGS shows robustness to the quality of the underlying QG and QA models, the domain of the models, and the number of questions asked. Even under the worst ablation settings, QAGS still has stronger correlation with human judgments than other automatic metrics.Overall, we contribute the following: (1) We introduce QAGS, an automatic model-based evaluation metric for measuring the factual consistency of model-generated text. (2) We collect a new set of human judgments of factual consistency of model-generated summaries for two summarization datasets. We demonstrate that QAGS correlates with these judgments significantly better than other automatic metrics. (3) We show via ablations that QAGS is robust to a number of factors including underlying model quality and domain mismatch. (4) We analyze the questions and answers produced in computing QAGS to illustrate which parts of summaries are inconsistent. (5) We will release models and code to compute QAGS.Standard approaches to evaluating generated text are primarily based on counting $n$-gram overlap. These methods assume access to one or more reference texts, and score a generated summary based on the precision and recall of all reference $n$-grams in the generated summary. We briefly describe the most common metrics in this family, and refer readers to BIBREF9 for further discussion.ROUGE BIBREF8 was developed specifically for evaluating automatic summarization, and its variants are the de facto standard for such. The most common variant is ROUGE-$n$ (typically $n \in \lbrace 1, 2\rbrace $), which computes the F1 score for all reference $n$-grams in the generated summary. ROUGE-$L$, another commonly used variant, is the length of the longest common subsequence (possibly non-consecutive) between a summary and references.BLEU BIBREF10 is closely related to ROUGE but was developed for machine translation. BLEU computes the precision of the reference $n$-grams in the generated summary. METEOR BIBREF11 extends BLEU by using an alignment between the generated text and a reference, as well as using stemming and synonym replacement for more flexible $n$-gram matching.We identify two key deficiencies when using these $n$-gram based evaluation metrics to detect factual inconsistencies in generated text.First, these metrics require one or more reference texts to compare against. Obtaining references can be expensive and challenging, and as such many text generation datasets contain only a single reference. This problem is exacerbated with high-entropy generation tasks, such as summarization or dialogue, where there is a very large number of acceptable outputs. In these settings, comparing against a single reference is woefully inadequate.Second, given a reference to compare against, $n$-gram based approach weigh all portions of the text equally, even when only a small fraction of the $n$-grams carry most of the semantic content. Factual inconsistencies caused by minor changes may be drowned out by otherwise high $n$-gram overlap, making these metrics insensitive to these errors. For example, the sentences “I am writing my paper in Vancouver.” and “I am not writing my paper in Vancouver.” share nearly all unigrams and bigrams despite having the opposite meaning.We introduce a framework for automatically detecting factual inconsistencies in generated text while also addressing the deficiencies of current approaches. Let $X$ and $Y$ be sequences of tokens coming from a vocabulary $V$ where $X$ is a source text and $Y$ is a summary of $X$. We define $p(Q|Y)$ as a distribution over all possible questions $Q$ given summary $Y$, and $p(A|Q, X)$ and $p(A|Q, Y)$ as distributions over all possible answers $A$ to a particular question $Q$ given either the source $X$ or the summary $Y$. We constrain the questions $Q$ and answers $A$ to also be sequences of tokens from $V$. Then the factual consistency of the summary $Y$ iswhere $D$ is some function measuring the similarity of the two answer distributions. This expression is maximized when $Y$ contains a subset of the information in $X$ such that it produces the same answer for any question from $p(Q|Y)$. This happens trivially when $Y=X$, e.g. we take $X$ as its own summary, but we usually have other desiderata of $Y$ such that this solution is undesirable.This framework addresses the two issues with $n$-gram based approaches. Instead of requiring a reference to compare against, our framework asks questions based on the generation itself, and compares answers with the provided source text. Also, the use of questions focuses the metric on the semantically relevant parts of the generated text, rather than weighting all parts of the text equally.In practice, exactly computing the expectation in Equation DISPLAY_FORM4 is intractable due to the large space of possible questions. One potential workaround is to randomly sample questions from $p(Q|Y)$, but this suffers from high variance and requires many samples to obtain a good estimate. Instead, we focus on producing highly probable questions, e.g. as produced by beam search, which may be biased in the limit, but will require fewer questions to estimate because of the higher quality of the questions.Using this framework requires specifying the question distribution $p(Q|Y)$, the answer distribution $p(A|Q, Y)$ (or $X$), and the answer similarity function $D$. We apply this framework to summarization to develop QAGS and describe our instantiations of these components.To instantiate $p(Q|Y)$, we draw on recent work on automatic question generation (QG), which models this distribution using neural seq2seq models BIBREF12, BIBREF13. We over-sample questions, and then filter out low quality questions as follows.First, we train and generate from answer-conditional QG models: The model receives both the answer and the source article, and is trained to maximize the likelihood of the paired question. At test time, we extract named entities and noun phrases as answers candidates using spaCy.Second, we filter out low-quality questions using a number of heuristics, such as duplicates and questions less than three tokens long. We also found it useful to run the QA model (see next section) on all of the candidate questions, and filter out questions for which the QA model predicted no answer.We instantiate the answer distributions $p(A|Q,*)$ as extractive QA models, for simplicity. We use extractive QA because we assume the facts are represented as text spans in the article and summary. Future work should explore using abstractive QA models, which could match paraphrases of the same answer.We use token-level F1 to compare answers, which is standard for extractive QA and equivalent to defining $D$ asGiven these components, we obtain the QAGS score of a generation by (1) generating $K$ questions conditioned on the summary, (2) answering the questions using both the source article and the summary to get two sets of answers, (3) comparing corresponding answers using the answer similarity metric, and (4) averaging the answer similarity metric over all questions. We depict this process in Figure FIGREF3.We test whether QAGS accurately measures the factual consistency of a summary with respect to a source article by computing correlations with human judgments of factual consistency.We evaluate on two abstractive summarization datasets, CNN/Daily Mail BIBREF0, BIBREF14 and XSUM BIBREF1. Abstractive summarization is particularly interesting because factual consistency with the original text is crucial to usability, and a lack of such consistency has plagued abstractive neural summarization models BIBREF15, BIBREF16, BIBREF5.CNN/DM is a standard dataset for summarization that consists of CNN and DailyMail articles. Each reference summary consists of the concatenation of three editor-written, bullet point highlights. For summaries, we use 235 test outputs from BIBREF17.XSUM was created by taking the first sentence of a news article as the summary, and using the rest of the article as the source. Consequently, XSUM summaries are significantly more abstractive than those of CNN/DM, and extractive summarization models perform poorly on this dataset.We found that while the XSUM summaries are more abstractive, frequently there are facts (e.g. first names) in the summary that are not available in the “article”. This quirk made it especially difficult for humans and QAGS to tell when factual errors were being made by the summarization model. To remedy this, for human evaluation and QAGS, we prepend the summary back to the “article”. We use a subset of 239 test outputs from BART fine-tuned on XSUM BIBREF2.We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18. We present summaries one sentence at a time, along with the entire article. For each summary sentence, the annotator makes a binary decision as to whether the sentence is factually consistent with the article. Workers are instructed to mark non-grammatical sentences as not consistent, and copies of article sentences as consistent. Workers are paid $1 per full summary annotated. See Appendix SECREF10 for further details.We collect 3 annotations per summary. To obtain a single “correctness” score per summary, we first take the majority vote for each sentence, then average the binary scores across summary sentences.Inter-annotator agreement as measured by Krippendorff's $\alpha $ is 0.51 and 0.34 for CNN/DM and XSUM, respectively indicating “moderate” and “fair” agreement BIBREF19. While not perfect, these agreement numbers are in-line with similar figures from previous work on summarization evaluation BIBREF4.We use fairseq BIBREF20 to fine-tune a pretrained BART language model on NewsQA BIBREF21, a dataset consisting of CNN articles and crowdsourced questions. For each summary, we use 10 answer candidates and generate questions using beam search with width 10, for a total of 100 question candidates. After filtering, we use the $K = 20$ most probable questions. If a summary has too few filtered questions, we randomly sample questions to reach the required number. For details, see Appendix SECREF11.We train QA models by fine-tuning BERT BIBREF6 on SQuAD2.0 BIBREF22. We use the large-uncased BERT variant via the transformers library BIBREF23.We compare against a number of automatic evaluation metrics: ROUGE BIBREF8, METEOR BIBREF11, BLEU BIBREF10, and BERTScore BIBREF24. The latter uses BERT representations to compute an alignment between generation and reference tokens, and which is then used to compute a soft version of unigram F1. We use the large-uncased BERT variant.We present results in Table . QAGS strongly outperforms other automatic evaluation metrics in terms of correlation with human judgments of factual consistency. BLEU and ROUGE perform comparably, and lower order $n$-gram metrics work better. BERTScore matches the best $n$-gram metrics on CNN/DM, but the worst overall on XSUM.On CNN/DM, QAGS obtains nearly twice the correlation of the next best automatic metric (BLEU-1). We speculate that this large increase is due to the sensitivity of the QA model to the sentence fusing behavior exhibited in many summarization models trained on CNN/DM BIBREF25. When two sentences are fused to produce an incorrect summary statement, the QA model produces different answers than when using the source article versus when using the summary.On XSUM, all metrics correlate worse with human judgments than on CNN/DM, which reflects the fact that XSUM is more abstractive. QAGS still outperforms the next best automatic metric.A potential issue with model-based evaluation is that the quality of the evaluation metric may depend heavily on specific hyperparameter settings. We explore whether this is true with QAGS by performing ablations on several factors.We first consider the degree to which the quality of the underlying models impacts their evaluation capabilities.For QA quality, we answer this question by training QA models of varying quality by fine-tuning different versions of BERT on SQuAD. We present results in Table . The QA models perform similarly despite substantially different performances on the SQuAD development set. Surprisingly, using the best QA model (bert-large-wwm) does not lead to the best correlations with human judgments. On CNN/DM, bert-large-wwm slightly underperforms bert-base and bert-large. On XSUM, bert-base slightly outperforms the other two BERT variants. These results indicate that QAGS is fairly robust to the quality of the underlying QA model, though we note that BERT is a strong QA baseline, and using weaker QA models might lead to larger performance dropoffs.To ablate QG quality, we use models with increasing perplexity on the NewsQA development set. Results in Table show that QAGS is robust to the QG model quality, with some decrease in correlation with human judgments as perplexity increases on CNN/DM, and no clear trend on XSUM. Even the weakest QG model still significantly outperforms all other automatic metrics in Table .Our approach relies on having a labeled dataset to train QG and QA models. However, for relatively niche domains, such a labeled QA/QG dataset may not exist. Instead, we may need to resort to using models trained on out-of-domain data, leading to domain shift effects that negatively impact the quality of the QAGS scores. We simulate this setting by fine-tuning the QG model on SQuAD, which is of similar size to NewsQA but drawn from Wikipedia articles rather than CNN articles, which exactly matches the genre of the summarization datasets.Evaluating with this QG model, we get correlations of 51.53 and 15.28 with human judgments on CNN/DM and XSUM respectively, versus 54.53 and 17.49 when using the NewsQA-tuned QG model. The drop in performance indicates a negative domain shift effect. However using the SQuAD-tuned QG model still substantially outperforms all other automatic metrics, again pointing to the robustness of QAGS.Next, we investigate the correlation with human judgments when varying the number of questions used. Results in Table show that increasing the number of questions used improves correlations with human judgments. We observe a large increase when moving from 10 to 20 questions, and a smaller increase from 20 to 50 questions, indicating decreasing marginal benefit moving beyond 50 questions. With just 5 questions, QAGS still substantially outperforms other automatic metrics, indicating its robustness.Finally, we consider using exact match as an alternative answer similarity metric. Exact match is another common evaluation metric for extractive QA, and is more restrictive than F1. When using EM, we obtain Pearson correlations with human judgments of 45.97 and 18.10 on CNN/DM and XSUM, as opposed to 54.53 and 17.49 when using F1.Several works explore the use of natural language inference (NLI) models to detect factual consistency in generated text BIBREF26, BIBREF16. We compare against these methods by evaluating on the sentence ranking experiment from BIBREF16. The experiment uses 373 triplets of source sentences from CNN/DM and two summary sentences generated from the model from BIBREF27. One summary sentence is factually consistent with the source sentence, and the other is inconsistent. A metric (or model) is evaluated based on how often it ranks the consistent sentence higher than the inconsistent sentence.We present the results in Table . Results using two NLI models fine-tuned on MultiNLI BIBREF28, BERT NLI and ESIM BIBREF29, are from BIBREF16. FactCC BIBREF5 is an NLI-based fact-checking model that is trained on a dataset tailor made for detecting factual inconsistencies in generated text. QAGS outperforms these methods, while requiring no special supervision for this task.The questions and answers produced in computing QAGS are directly interpretable, and highlight errors in summaries. We present examples of articles, summaries, and the QAGS questions and answers in Table .On the first example (Table , top), QAGS detects several factual inconsistencies in the generated summary: The summary mistakes the first name of the attacker, the location of the attack, and the weapons used. Because the QG model focuses on these details, QAGS is able to correctly penalize the summary for its hallucinations. Because the answer candidates used are mostly named entities and noun phrases, QAGS is particularly effective at detecting errors of this kind. Using more diverse answer candidates may broaden the set of inconsistencies that QAGS is able to detect.The second example (Table , bottom), illustrates failure modes of QAGS. For example, the QA model incorrectly marks question 2 as unanswerable. On question 4, both answers produced are correct, but because they have no common tokens, they are marked inconsistent by QAGS.The interpretability of QAGS allows for error analysis on the metric. We manually annotate 400 triplets of generated questions, article answers, and summary answers that are produced in computing QAGS on the XSUM summaries, and label them by the quality of the generated questions, predicted answers, and answer similarity scores.Among the generated questions, 8.75% are nonsensical, while 3.00% are well-formed but unanswerable using the generated summary they were conditioned upon. These figures indicate that the vast majority of questions are understandable and on-topic. We frequently observe multiple questions with slightly different wordings, which is likely due to the low number of answer candidates in XSUM summaries (which are one sentence long) and due to beam search. 8.25% of questions are well-formed but unanswerable using the source, which is usually due to a hallucinated fact in the summary that the QG model turns into a question.Among predicted answers, 1.75% of questions are potentially answerable using the summary, but are incorrectly answered. This percentage increases to 32.50% for the article, which indicates that the transfer ability of the QA model is lacking. In a small number of cases, we found that while a question had a single answer in the summary, it could have multiple answers in the article.Finally, for 8.00% of the examples, the question is answered correctly using both the article and summary, but the answers have high lexical variation such that F1 score fails to detect their similarity. While this happens in a relatively small number of cases, exploring similarity metrics other than $n$-gram based approaches could be useful.We emphasize that QAGS and our overall framework are specifically designed to detect factual inconsistencies in generated summaries relative to the source article. QAGS does not measure other desirable properties of generated text, including fluency, readability, or factual recall. We therefore recommend using QAGS in conjunction with complementary evaluation metrics.The choices of QG and QA models in QAGS are particular to abstractive summarization and may require adaptation to be used for other conditional text generation tasks. For example, we expect that extractive summarization models may obtain nearly perfect QAGS scores because facts and statements are directly copied from the source article.Automatic summarization and its evaluation are long-standing lines of work in NLP, dating at least as far back as the Document Understanding Conferences BIBREF30. The primary evaluation metric then and now is ROUGE BIBREF8, though much work has demonstrated the limited ability of ROUGE and its relatives to evaluate summaries BIBREF31, BIBREF32, BIBREF33. Other metrics have focused on specific aspects of summarization quality, including content selection BIBREF34, relevance prediction BIBREF4, and many more.There has been a recent resurgence of work leveraging NLU models for evaluating the factuality of generated text. BIBREF35 use information extraction models to measure factual overlap, but facts are restricted to pre-defined schemas. BIBREF16 investigate the use of NLI models to evaluate the factual correctness of CNN/DM summaries, and conclude that current NLI models are too brittle to be reliably used in this manner. BIBREF5 train a NLI-based fact-checking model by building a dataset of factual inconsistencies based on noise heuristic. Our QA approach allows a finer-grained analysis, because NLI operates on complete sentences, whereas QAGS can ask many questions about the same sentence.Most relatedly, BIBREF36 and BIBREF37 use QA models to evaluate summarization. We diverge from these works in two important ways. First, both works use Cloze-style questions, which are generated by masking entities in either the source document or the reference summary. We instead generate the questions with a model, allowing a much greater range of questions. Second, we produce questions conditioned on the generated summary, rather than the reference summary or source article. Producing questions from the generated summary is more appropriate for verifying the accuracy of the text, whereas using the reference or source measures content selection.We introduce a framework for automatically detecting factual inconsistencies in conditionally generated texts and use this framework to develop QAGS, a metric for measuring inconsistencies in abstractive summarization. QAGS correlates with human judgments of factuality significantly better than standard automatic evaluation metrics for summarization, and outperforms related NLI-based approaches to factual consistency checking. QAGS is naturally interpretable: The questions and answers produced in computing QAGS indicate which tokens in a generated summary are inconsistent and why. Error analysis shows that future work should explore improved QA models. Our approach can also be applied to diverse modalities, such as translation and image captioning. Overall, we believe QAGS is useful in quantifying and incentivizing factually consistent text generation.We restrict our pool of workers to US-based workers. Workeres are required to have at least 1000 approved HITs with an acceptance rate of at least 98%.The base reward for our task is $0.15. For each summary, we include automatic quality checks includingTime checks: workers who complete the task under 30s fail the checkAttention checks: we include exact copies of article sentences and corrupted mixtures of two article sentences as positive and negative control task. If a worker fails to answer both of these examples correctly, they fail the checkExplanation checks: For each sentence in the summary, the worker is required to provide a short explanation of their decisionIf a worker passes all checks, they are awarded a $0.85 bonus, totalling $1.00 per correct annotation. According to turkerview.com, workers of our HIT are paid well in excess of $15.00 on average.We show our annotation interfaces for the annotation task for CNN/DM and XSUM respectively in Figures FIGREF27 and FIGREF28. We use slightly different instructions to accommodate for the quirks of each dataset. For XSUM, we prepend the reference “summary” back onto the source article, as without it, workers were struggling to identify factual inconsistencies.We fine-tune BART for question generation using the same tuning hyperparameters as the original work. We optimize label smoothed cross entropy with smoothing parameter 0.1 BIBREF41 and a peak learning rate of 2e-5. We optimize for 100k steps with 5k warmup steps, and use the model with the best perplexity on the development set.To turn NewsQA into an answer conditional QG dataset, we concatenate the answer to the source article with a special marker token in between. We then concatenate another special marker token and the question. At test time, we get 10 named entities and noun phrases as answer candidates using the en-web-sm spaCy model. We downsampling if there are more than 10 and randomly duplicating some answers if there are more than 10. The model predicts the question after seeing an answer and the article.During decoding, we use beam search with beam size 10, length penalty 1.0, and trigram repetition blocking. We experimented with top-$k$ BIBREF39 and top-$p$ BIBREF38, but the outputted questions, while diverse, were quite noisy. Generations have minimum length 8 and max length 60.To filter the questions, we first use simple heuristics, including removingeverything after the first question mark in a questionexact duplicatesquestions shorter than three tokens longFor the remaining questions, we use our QA model to answer each question and we remove questions for which the QA model deems unanswerable. We then take the top 20 most probable questions, random sampling some of the filtered questions if there were too few.We fine-tune BERT for question answering following the original work. We optimize using AdamW BIBREF40 with initial learning rate 5e-5. We train for 3 epochs, with a warmup ratio of 0.1. We use the model with the best development set performance.We use SQuAD2.0 because we found the unanswerable questions useful for filtering out questions and questions based on hallucinated facts in the summary should be unanswerable using the source article. Similar to the QG setting, we append the question and answer to the source article with intervening special marker tokens.

Q: What models are evaluated with QAGS?

Q: Do they use crowdsourcing to collect human judgements?