Shortcomings of Question Answering Based Factuality Frameworks for Error Localization (2023)

Abstract: Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factuality and have potential to localize errors within summaries, but this latter capability has not been systematically evaluated in past research. In this paper, we conduct the first such analysis and find that, contrary to our expectations, QA-based frameworks fail to correctly identify error spans in generated summaries and are outperformed by trivial exact match baselines. Our analysis reveals a major reason for such poor localization: questions generated by the QG module often inherit errors from non-factual summaries which are then propagated further into downstream modules. Moreover, even human-in-the-loop question generation cannot easily offset these problems. Our experiments conclusively show that there exist fundamental issues with localization using the QA framework which cannot be fixed solely by stronger QA and QG models.

## introduction although abstractive summarization systems @xcite @xcite have improved drastically over the past few years, these systems often introduce factual errors into generated summaries @xcite . recent work has proposed a number of approaches to detect these errors, including using off-the-shelf entailment models @xcite , question answering (qa) models @xcite @xcite , and discriminators trained on synthetic data @xcite . such methods have also been explored to identify error spans within summaries @xcite source article: my recent exhibition features some prominent trends and themes spanning the entire history of the matchbox industry. i exhibited 5,000 labels from my collection of 25,000. for the past 15 years, i have been collecting matchbox labels. bart/ pegasus since when have i been collecting labels? what have i been collecting for 15 years? question generation -question answering framework unanswerable unanswerable questions are generated for summary spans and then answered by a qa model using the source article as context. for factual spans (e.g. matchbox labels), we expect the predicted answers to match the original spans. however, non-factual spans in generated questions inherited from summaries may render these unanswerable and lead to incorrect error localization. and perform post-hoc error correction @xcite . among these different approaches for evaluating factuality, qa-based frameworks are the most widely adopted @xcite @xcite @xcite . these evaluate the factuality of a set of spans in isolation, then combine them to render a summary-level judgment. figure 1 illustrates the core mechanism: question generation (qg) is used to generate questions for a collection of summary spans, typically noun phrases or entities, which are then compared with those questions' answers based on the source document to determine factuality. due to this spanlevel decomposition of factuality, qa frameworks are widely believed to localize errors @xcite @xcite . therefore, the metrics have been applied in settings like post-hoc error correction @xcite , salient @xcite and incorrect @xcite span detection, and text alignment @xcite . however, their ac-tual span-level error localization performance has not been systematically evaluated in prior work. in this paper, we aim to answer the following question: does the actual behavior of qa-based metrics align with their motivation? specifically, we evaluate whether these models successfully identify error spans in generated summaries, independent of their final summary-level judgment. we conduct our analysis on two recent factuality datasets @xcite derived from pre-trained summarization models on two popular benchmark datasets: cnn/dm @xcite and xsum @xcite . our results are surprising: we find that good summary-level performance is rarely accompanied by correct span-level error detection. moreover, even trivial exact match baselines outperform qa metrics at error localization. our results clearly show that although motivated by span-level decomposition of the factuality problem, the actual span-level predictions of qa metrics are very poor. next, we analyze these failure cases to understand why qa-based metrics diverge from their intended behavior. we find that the most serious problem lies in the question generation (qg) stage: generated questions for non-factual summaries inherit errors from the input summaries (see figure 1 ). this results in poor localization wherein factual spans get classified as non-factual due to presupposition failures during qa. furthermore, we show that such inherited errors cannot be easily avoided: decreasing the length of generated questions reduces the number of inherited errors, but very short questions can be under-specified and not provide enough context for the qa model. in fact, replacing automatic qg with human qg also does not improve the error localization of qa metrics. these results demonstrate fundamental issues with the current qa-based factuality frameworks that cannot be patched by stronger qa/qg methods. our contributions are as follows. (1) we show that qa-based factuality models for summarization exhibit poor error localization capabilities. (2) we provide a detailed study of factors in qg that hamper these models: inherited errors in long generated questions and trade-offs between these and short under-specified questions. (3) we conduct a human study to illustrate the issues with the qa-based factuality framework independent of particular qa or qg systems. ## qa-based factuality metrics recent work has proposed numerous qa-based metrics for summarization evaluation, particularly factuality @xcite @xcite @xcite . these proposed metrics follow the same basic framework (described in section 2.1), and primarily differ in the choice of off-the-shelf models used for the different framework components (discussed in section 2.2). ## summary vs. span level performance qa metrics motivate the use of span-level factuality as building blocks for evaluating factuality at the sentence level. therefore, our hypothesis is that good summary-level performance must be accompanied by good span-level performance. here, we test this by comparing summary-and span-level performances. figure 3 outlines the performance of qa metrics and baseline systems. the top row shows roc curves and f1 scores (in the legend) for all three datasets; the bottom row shows span-level results. the dotted black lines show the performance of a random baseline. first, we observe that none of the baselines or qa metrics have a clear advantage over other systems for all datasets at the summarylevel. for instance, qa metrics outperform baselines on gd21, and show similar performance on cliff (cnn/dm) and worse performance on the cliff (xsum) dataset. however, across all datasets, we see that there exists a substantial mismatch between the performance of qa metrics at the summary-and span-levels. notably, for gd21, both qe and qafe substantially outperform baseline models at the summary-level, but exhibit much poorer span-level performance. similarly, qa metrics are comparable to baselines at the summary-level for cliff (cnn/dm) but much worse at the span-level. on the other hand, the error localization performance of the dae model is more consistent with its summary-level performance. surprisingly, the trivial exact match (em) baseline consistently outperforms qa metrics at error localization for all datasets. these results clearly show that our hypothesis is false: qa-based metrics do not provide reliable span-level explanations for their summary-level predictions. note that the diagonal lines in the span-level roc curves for qa metrics arise due to a large number of spans being assigned the same factuality scores. as discussed in section 2.1, some spans are filtered during the question filtering stage (step 3) if their corresponding generated questions are of low quality. we consider these to be factual and assign them the maximum factuality score; this results in the diagonal line from (0, 0). 3 we study the effects of this span filtering on localization performance in appendix c. additionally, qafe assigns the same factuality score (= 0) to all spans with unanswerable questions resulting in the diagonal line to (1, 1). consider the error localization task in the example in figure 1 . here, the qa metric needs to correctly distinguish between the factual span "matchbox labels" and the extrinsic error "for the past 15 years". for such summaries (containing a mix of factual and non-factual spans), we observed that the generated questions for factual spans often inherit non-factual summary spans. given such questions, e.g. "what have i been collecting for 15 years?", an ideal qa model should predict unanswerable (even though that hurts localization) as the source article does not include any mention of an item being collected for 15 years. based on this obser-summary: for the past 15 years, i have been collecting matchbox labels .