Learning with Rejection for Abstractive Text Summarization (2022)

Abstract: State-of-the-art abstractive summarization systems frequently hallucinate content that is not supported by the source document, mainly due to noise in the training dataset.Existing methods opt to drop the noisy samples or tokens from the training set entirely, reducing the effective training set size and creating an artificial propensity to copy words from the source. In this work, we propose a training objective for abstractive summarization based on rejection learning, in which the model learns whether or not to reject potentially noisy tokens. We further propose a regularized decoding objective that penalizes non-factual candidate summaries during inference by using the rejection probability learned during training.We show that our method considerably improves the factuality of generated summaries in automatic and human evaluations when compared to five baseline models, and that it does so while increasing the abstractiveness of the generated summaries.

## introduction abstractive summarization has progressed significantly in recent years, as evidenced by automatic evaluation scores like rouge @xcite . however, existing abstractive summarization systems are prone to produce non-factual summaries, which contain information that is not supported by the source @xcite @xcite @xcite . according to recent studies, one cause of this phenomenon is noise in the training set, such as unsupported facts in the references @xcite @xcite . specifically, the widely-used negative log likelihood objective aims to match the underlying data distribution. if summarization models are trained on noisy datasets containing erroneous references, they will tend to imitate and even amplify undesired generations @xcite . different approaches have been proposed to reduce non-factual hallucinations. @xcite ; @xcite employ re-ranking based approaches to up-rank candidate summaries based on their factuality scores from out-of-the-box factual consistency evaluation systems. @xcite ; @xcite ; @xcite use loss truncation-based method that drops high loss sentences/tokens during training. @xcite propose reinforcement learning (rl)-based methods that penalize hallucinated tokens with negative rewards. one instance of the reference summary containing unsupported information is shown in in this work, we propose a training objective that enables the summarization model to recognize and reject unsupported target text spans. inspired by rejection learning for image classification @xcite @xcite , we introduce a new rejection class at training time that offers the model an option to reject confusing tokens rather than assigning high probability to all reference tokens. besides, our method does not require labeling or data preprocessing. the learning is based on the training our main contributions in this work are: (i) we introduce a rejection learning objective for training abstractive summarization models. we demonstrate how the additional training objective enables the summarization model to precisely reject hallucinated tokens during inference (section 5.1). ( ii) we propose a novel factuality-aware regularized decoding objective based on the rejection probability learned during training. our method significantly improves the factuality of generated summaries in automatic and human evaluations when compared to five baseline models. (iii) we demonstrate that, in comparison to the baselines, the summaries produced by our method also exhibit a greater level of abstractiveness with more novel n-grams and less copying. ## related work with the advent of transformer-based summarization models @xcite @xcite @xcite , current system-generated summaries are often fluent and salient. however, detailed analysis of these systems' outputs reveals that the majority of the generation contains hallucinations @xcite . moreover, annotations from @xcite suggest that the majority of these hallucinations are extrinsic; namely, they contain information that is not directly inferable from the source. many prior studies consider extrinsic hallucinations generated by models as undesirable, and propose different methods to reduce these hallucinations @xcite @xcite @xcite . on the other hand, some prior work @xcite @xcite suggest many of these hallucinations are actually factual and can be useful to provide additional background information. the most similar work to ours is the approach of using loss truncation to reduce noise in the training stage. @xcite and @xcite propose sentence and token-level loss truncation for hallucination reduction. these strategies are based on the assumption that noisy examples are difficult to learn, resulting in high training loss. simply removing these difficult-to-learn high log loss samples might reduce model hallucination. however, dropping too many samples could make the training process inefficient. it also makes the summarization model more extractive. dropping high loss token might not be effective either since the model does not learn the right token to generate during decoding. we instead use the so-called rejection (or abstention) approach to combat noise in training. the loss abstention approach was proposed in @xcite for classification tasks, where a classifier is trained to abstain on mislabeled images during training. inspired by this idea, we introduce a rejection loss for generation tasks (section 3.1). the advantage of loss abstention over loss truncation is its ability to learn text features that are likely to lead to hallucination rather than simply ignore them. such features can be used during inference to avoid hallucinations. ## conclusion in this work, we propose a rejection learning based training objective for abstractive summarization models that is robust to the presence of noise in the dataset. the proposed training objective allows the model to learn features that are associated with the noise and reject them during training. we additionally propose a novel regularized decoding objective that effectively penalizes non-factual can-didate summaries during inference. we show that our method vastly improves the factuality of generated summaries over five baseline models using both automatic and human evaluation. further analysis reveals that our model enhances the factuality of the summaries while simultaneously increasing the abstractiveness of the generated summaries.