Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022)

Abstract: Current abstractive summarization systems tend to hallucinate content that is unfaithful to the source document, posing a risk of misinformation. To mitigate hallucination, we must teach the model to distinguish hallucinated summaries from faithful ones. However, the commonly used maximum likelihood training does not disentangle factual errors from other model errors. To address this issue,we propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. Specifically, we train an elaboration model that generates hallucinated documents given the reference summaries, and then generates negative summaries from the fake documents. We incorporate the negative samples into training through a controlled generator, which produces faithful/unfaithful summaries conditioned on the control codes. Additionally, we find that adding textual entailment data through multitasking further boosts the performance. Experiments on three datasets (XSum, Gigaword, and WikiHow) show that our method consistently improves faithfulness without sacrificing informativeness according to both human and automatic evaluation

## introduction despite the fast progress in fluency and coherence of text summarization systems, a common challenge is that the generated summaries are often unfaithful to the source document, containing hallucinated, non-factual content @xcite . current summarization models are usually trained by maximum likelihood estimation (mle), where unfaithful and faithful summaries are penalized equally if they both deviate from the reference. as a result, when the model fails to imitate the reference, it is likely to "over-generalize" and produce hallucinated content. in this work, we address the issue by explicitly teaching the model to discriminate between positive (groundtruth) and negative (unfaithful) summaries. the key challenge is to generate realistic negative samples. existing work on negative data augmentation mainly focuses on corrupting the reference (e.g., replacing entities) or sampling low-probability model outputs @xcite @xcite . however, the synthetic data often does not resemble actual hallucinations from the model @xcite and many methods rely on external tools such as ner taggers. to generate unfaithful summaries, we propose a simple method inspired by back-translation @xcite (fig. 1 ). specifically, we first generate fake documents using an elaboration model that is trained to produce a document given the summary. we then generate summaries from the fake documents, which are assumed to be unfaithful since they are likely to contain hallucinated information in the fake documents. given the reference summaries and the augmented negative samples, we train a controlled generation model that generates either faithful or unfaithful summaries conditioned on a faithfulness control code. at inference time, we control the model to generate only faithful summaries. we call our approach cofe (controlled faithfulness via elaboration). the controlled generation framework allows us to incorporate additional data easily: jointly training on natural language inference (nli) datasets to generate entailed (faithful) and non-entailed (unfaithful) hypotheses further improves the result. we evaluate cofe on three summarization datasets: xsum @xcite , gigaword @xcite , and wikihow @xcite . both automatic metrics and human evaluation show that our method consistently outperforms previous methods in terms of faithfulness and content similarity to the reference, without sacrificing abstractiveness @xcite . ## approach to learn a summarization model, the commonly used mle aims to imitate the reference and does not distinguish different types of errors, thus the model may be misaligned with the desired behavior in downstream applications. for example, a faithful summary missing a detail would be preferred over a summary with hallucinated details, even if both have low likelihood under the data distribution. therefore, additional inductive bias is needed to specify what unfaithful summaries are. therefore, we augment negative examples and jointly model the distributions of both faithful and unfaithful summaries. at decoding time, we generate the most likely faithful summary. ## related work recent work in automated factuality metrics @xcite @xcite has spurred interests in building more faithful systems. prior work has tackled the problem from the aspects of data, modeling, and learning. data. since most summarization datasets are scraped online, there may be unfaithful summaries in the training data. thus, one approach is to filter the training data to remove noisy summaries or tokens. for example, kang and hashimoto (2020) drop high-loss examples from training, observing that these examples are usually of lower quality. @xcite discard sentences from gold summaries if there is an entity that does not match the entities in the document. goyal and durrett (2021) take a more fine-grained approach, and use a dependency arc-based entailment metric @xcite to filter noisy tokens from the summary. ## conclusion we present cofe, a data construction and training pipeline to improve faithfulness of summarization system. in the negative sample generation stage, fabricated details are generated through the elaborator, and some of them will be kept by summarizor in negative samples. in the training stage, cofe adopts the prefix-control framework, which is designed to provide conditions through different prefixes, so as to make the model distinguish between unfaithfulness and faithfulness. our experiments show that by add nli data into training, faithfulness can be further enhanced.