Question: In 3.1, what is the actual list of metrics used in this study? Also mixes experimental setup with method description.
Evidence: Neural abstractive summarization systems trained by maximizing the likelihood of a reference summary (MLE) given its source document have been shown to generate plausible summaries with high lexical overlap with the references. However, human analyses and automatic evaluations show that state-of-the-art neural models, trained on widely used XSUM and CNN/DM datasets, tend to hallucinate information with high frequency. For instance, by performing human evaluations on 2250 model generated summaries from CNN/DM and XSUM datasets, found that 60% of the summaries contained at least one factual error. The hallucinations are broadly classified as extrinsic, when a model adds information that is not present in the source document, and intrinsic, when the model distorts information present in the source document into a factually incorrect representation. The type and degree of a model's hallucinations correlate with the quality of training data. As noted by, models trained on the XSum data, which include extrinsic hallucinations in reference summaries, tend to generate a higher proportion of extrinsic hallucination as compared to models trained on the cleaner CNN/DM dataset.
In this paper, we propose the Mixture of Factual Experts (MoFE), a simple framework that applies an ensemble of factual experts to control hallucinations in summarization systems. We define factual expert as a model that generates summaries with certain desirable factual qualities (e.g. fewer extrinsic hallucinations). Each constituent factual expert in MoFE is trained to target a unique type of factual quality. The training of the experts is motivated by two broad observations. First, the data on which the model is trained may influence the factual consistency of the model. Therefore, we employ a data pre-processing step that filters training samples such that the references exhibit the desirable factual qualities. Second, the maximum-likelihood loss function may overlook factual consistency. Therefore we employ reinforcement learning (RL) to train a model using explicit signals of factual consistency. Further, we augment standard reward-based RL loss with a KL divergence loss between the expert and pre-trained model's distributions on pivot summary 1 to prevent the former from deviating too far from the latter. We propose to choose the pivot summary depending on the number of factual errors in training samples. When all samples in the training data possess desirable factual consistency, we use reference as the pivot summary. On the contrary, if training data contains factual errors, we use the expert-sampled summary as the pivot. We show the schematic view of MoFE in Figure.
We use entity overlap and dependency arc entailment (DAE) accuracy metrics as measures of extrinsic and intrinsic hallucinations, respectively, and accordingly use both metrics to define rewards for training experts targeting both types of hallucination. Entity overlap evaluates the number of entities in summary that are absent from the source document and is a direct measure of extrinsic hallucination. Intrinsic hallucination, on the other hand, is broader and includes errors such as incorrect predicates or their arguments, coreference errors, discourse link errors, etc.. Since DAE accuracy measures the fine-grained entailment relations at the dependency arc level, we consider it a reasonable proxy for measuring intrinsic hallucinations. Additionally, given that experts trained on both entity overlap and DAE metrics try to improve precision and are prone to reducing factual recall, MoFE also includes an entity recall-based expert. Subsequently, we combine the above three experts through logits and weights ensembling.
We evaluate our MoFE on the two benchmark abstractive summarization datasets, XSUM and CNN/DM. We use a diverse set of metrics, including entailment, entity overlap, and question answering (QA)-based metrics to measure factual errors. We find that MoFE models strongly outperform the state-of-the-art BART model, obtaining up to ∼ 6% absolute improvement on factual consistency metric (summary-level DAE accuracy) used to train experts, with marginal degradation (< 0.74) on ROUGE scores. Further, MoFE performs better than BART on two QAbased metrics (FEQA and QuestEval) on XSUM dataset. However, on CNN/DM, we find contrasting results, MoFE improves on QuestEval but not on FEQA, which may be attributed to the differences in questions generated by the two systems. Finally, we use the SummVis tool to analyze MoFE and BART models' summaries on the XSUM dataset. We find that, relatively, MoFE reduces factual errors, some of which are clear cases of hallucinations while others may represent world knowledge (e.g. replacing the UK with the United Kingdom). In the appendix, we show some example cases where MoFE-generated summary removed, added, or ignored factual errors in the BART-generated summary.
Therefore, we use DAE accuracy3 to identify cases of intrinsic hallucinations, both during training and evaluation.****First, we find that ensembling multiple BART models improves ROUGE scores and NER recall, but not factual consistency metrics defined by DAE accuracy and NER precision.****Separately, we also evaluate the MoFE on BERTScore (Zhang et al., 2019b) precision (BS-P) and recall (BS-R) 296 with respect to source and two question answer-based evaluation metrics, FEQA and QuestEval.
Answer: We have mentioned at multiple places that we use DAE, NER precision and recall metrics to train experts. Section 3.1 aims to describe possible metrics that are available and our rationale behind using DAE and NER based metrics. Sorry, if you found that confusing.