尽管取得了重大进展，但最先进的抽象摘要方法仍然易于与源文件不一致的幻觉内容。在本文中，我们提出了约束的抽象摘要（CAS），这是一种常规设置，它通过将令牌指定为摘要中必须存在的约束来保留抽象总结的事实一致性。我们采用词汇限制解码，一般适用于自回归的生成模型的技术，履行CAS并在两种情况下进行实验：（1）自动摘要而不进行人类参与，其中短语从源文档中提取并用作约束; （2）人性引导的交互式摘要，其中人工制约形式的人类反馈用于指导摘要一代。两个基准数据集上的自动和人为评估表明CAS改善了词汇重叠（胭脂）和抽象总结的事实一致性。特别是，当在交互式摘要中仅使用一个手动约束时，我们将观察到最多13.8胭脂-2增益。

尽管最近的抽象性摘要在自动评估指标上取得了成功，但生成的摘要仍然与源文档呈现事实不一致。在本文中，我们专注于实体级别的事实不一致，即减少生成的摘要与源文档之间的不匹配实体。因此，我们提出了一种基于实体的新型跨度机制，并通过全球相关成分探索其扩展。四个摘要数据集的实验结果表明，跨度可以有效地改善实体级别的事实一致性，而单词级别和实体级别的显着性基本上没有变化。该代码可在https://github.com/wendy-xiao/entity基于基础上找到

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.

最先进的抽象摘要系统经常生成\ emph {幻觉};即，不直接从源文本中推断的内容。尽管被认为是不正确的，我们发现非常令人难潮的内容是事实，即与世界知识一致。这些事实幻觉通过提供有用的背景信息，可以在摘要中受益。在这项工作中，我们提出了一种新的检测方法，将事实与实体的非事实幻觉分开。我们的方法分别使用实体的先前和后验概率，分别是预训练和芬特的屏蔽语言模型。经验结果表明，我们的方法在精度和F1分数方面大大优于两种基线％，与人类判断强烈相关。百分比对事实分类任务。此外，我们显示我们的探测器，当用作离线增强学习（RL）算法中的奖励信号时，显着提高了摘要的事实性，同时保持抽象水平。

Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation protocols and benchmarks for summarization either exhibit low inter-annotator agreement or lack the scale needed to draw statistically significant conclusions, and an in-depth analysis of human evaluation is lacking. In this work, we address the shortcomings of existing summarization evaluation along the following axes: 1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which relies on fine-grained semantic units and allows for high inter-annotator agreement. 2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of over 22k summary-level annotations over state-of-the-art systems on three datasets. 3) We compare our ACU protocol with three other human evaluation protocols, underscoring potential confounding factors in evaluation setups. 4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. Furthermore, our findings have important implications for evaluating large language models (LLMs), as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators' prior, input-agnostic preferences, calling for more robust, targeted evaluation methods.

利用预训练语言模型的抽象摘要系统在基准数据集上取得了卓越的结果。但是，此类模型已被证明更容易幻觉，这些事实对输入背景不忠。在本文中，我们提出了一种通过实体覆盖范围控制（ECC）来补救实体级外部幻觉的方法。我们首先计算实体覆盖范围的精度，并为每个培训示例提供相应的控制代码，该示例隐含地指导该模型在训练阶段识别忠实的内容。我们通过从Wikipedia提取的大但嘈杂的数据中进行中间调整进一步扩展了我们的方法，以解锁零击摘要。我们表明，根据我们对三个基准数据集XSUM，PubMed和Samsum的实验结果，根据我们在监督的微调和零射击设置中，可以在监督微调和零摄像设置中更加忠实和显着的抽象性汇总。

State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary's essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38%. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where post-editors show the largest improvements.

Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several recent efforts attempt to address this by devising models that automatically detect factual inconsistencies in machine generated summaries. However, they focus exclusively on English, a language with abundant resources. In this work, we leverage factual consistency evaluation models to improve multilingual summarization. We explore two intuitive approaches to mitigate hallucinations based on the signal provided by a multilingual NLI model, namely data filtering and controlled generation. Experimental results in the 45 languages from the XLSum dataset show gains over strong baselines in both automatic and human evaluation.

Despite the recent progress in language generation models, their outputs may not always meet user expectations. In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment. To this end, we consider factual consistency in summarization, the quality that the summary should only contain information supported by the input documents, for user preference alignment. We collect a high-quality dataset, DeFacto, containing human demonstrations and informational feedback in natural language consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary. Using our dataset, we study two natural language generation tasks: 1) editing a summary using the human feedback, and 2) generating human feedback from the original summary. Using the two tasks, we further evaluate if models can automatically correct factual inconsistencies in generated summaries. We show that the human-edited summaries we collected are more factually consistent, and pre-trained language models can leverage our dataset to improve the factual consistency of original system-generated summaries in our proposed generation tasks. We make the DeFacto dataset publicly available at https://github.com/microsoft/DeFacto.

在这项工作中，我们提出了一种将问题回答（QA）信号纳入摘要模型的方法。我们的方法通过自动生成由NPS回答的WH问题并自动确定在黄金摘要中是否回答这些问题，识别输入文档中的显着名词短语（NPS）。基于QA的信号被纳入了一种双级摘要模型，该模型首先使用分类模型在输入文档中标记突出NPS，然后有条件地生成摘要。我们的实验表明，使用基于QA的监督训练的模型产生了比在基准摘要数据集上识别突出跨度的基线方法的高质量摘要。此外，我们示出可以基于输入文档中标记的NPS来控制所产生的摘要的内容。最后，我们提出了一种增强培训数据的方法，因此黄金摘要与培训期间使用的标记的输入跨度更加一致，并展示了如何在学习更好地排除未标记的文档内容的模型中的结果。

In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries. In this paper, we introduce Socratic pretraining, a question-driven, unsupervised pretraining objective specifically designed to improve controllability in summarization tasks. By training a model to generate and answer relevant questions in a given context, Socratic pretraining enables the model to more effectively adhere to user-provided queries and identify relevant content to be summarized. We demonstrate the effectiveness of this approach through extensive experimentation on two summarization domains, short stories and dialogue, and multiple control strategies: keywords, questions, and factoid QA pairs. Our pretraining method relies only on unlabeled documents and a question generation system and outperforms pre-finetuning approaches that use additional supervised data. Furthermore, our results show that Socratic pretraining cuts task-specific labeled data requirements in half, is more faithful to user-provided queries, and achieves state-of-the-art performance on QMSum and SQuALITY.

Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between the summary and source document. However, developing systems that can generate summaries with customizable semantic coverage is still an under-explored topic. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We take events as the basic semantic units of the source documents and propose to rank these events by their salience. We also develop a model to summarize input documents with given events as anchors and hints. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner. Meanwhile, we annotate a new benchmark GranuDUC that contains multiple summaries at different granularities for each document cluster. Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. Further, by exploiting the event information, GranuSum also exhibits state-of-the-art performance under the conventional unsupervised abstractive setting. Dataset for this paper can be found at: https://github.com/maszhongming/GranuDUC

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

生成事实 - 一致的摘要是抽象总结的具有挑战性的任务。以前的作品主要编码事实信息或在解码后执行校正后/等级。在本文中，我们从对比学习的角度提供了一个事实 - 一致的解决方案，这是之前作品的自然延伸。我们提出CO2SUM（对比一致性），一种对比的学习方案，可以很容易地应用于事实 - 一致的抽象总结的序列模型，证明了模型可以在不修改架构的情况下感知。 CO2SUM在编码器上应用对比度学习，该编码器可以帮助模型意识到输入文章中包含的事实信息，或者对解码器进行对比学习，这使得模型生成事实正确的输出摘要。更重要的是，这两种方案是正交的，可以组合以进一步改善忠诚。关于公共基准测试的综合实验表明，与其他强大的事实 - 一致的摘要基线相比，CO2SUM提高了大型预先训练的语言模型的忠诚，并达到竞争力。

深度神经语言模型的最新进展与大规模数据集的能力相结合，加速了自然语言生成系统的发展，这些系统在多种任务和应用程序上下文中产生流利和连贯的文本（在各种成功程度上）。但是，为所需的用户控制这些模型的输出仍然是一个开放的挑战。这不仅对于自定义生成语言的内容和样式至关重要，而且对于他们在现实世界中的安全可靠部署至关重要。我们提出了一项关于受约束神经语言生成的新兴主题的广泛调查，在该主题中，我们通过区分条件和约束（后者是在输出文本上而不是输入的可检验条件），正式定义和分类自然语言生成问题，目前是可检验的）约束文本生成任务，并查看受限文本生成的现有方法和评估指标。我们的目的是强调这个新兴领域的最新进展和趋势，以告知最有希望的方向和局限性，以推动受约束神经语言生成研究的最新作品。

以查询为中心的摘要（QFS）旨在产生应答感兴趣的特定问题的摘要，从而实现更大的用户控制和个性化。虽然最近发布的数据集如QMSUM或Aquamuse，促进QFS中的研究工作，但该领域缺乏对适用建模方法的广泛空间的全面研究。在本文中，考虑到两种普遍的方法，我们对QFS进行了系统探索，探讨了QFS：两阶段的采掘解决方案和端到端模型。在这些类别中，我们调查现有方法，并呈现了在QMSUM数据集上实现最先进的性能的两个模型扩展，其边缘高达3.38 Rouge-1，3.72 Rouge-2和3.28 Rouge-L。通过定量实验，我们突出了不同模型配置之间的权衡，并探讨了摘要任务之间的转移能力。代码和检查点公开可用：https：//github.com/salesforce/query-focused-sum。

Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties of generated summaries and potentially improve summarization quality (since more information are given). Unfortunately, control signals are not already available during inference time. In this paper, we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled (without control signals) modes. During training, Lotus learns latent prompt representations from prompts with gold control signals using a contrastive learning objective. Experiments show Lotus in uncontrolled mode consistently improves upon strong (uncontrollable) summarization models across four different summarization datasets. We also demonstrate generated summaries can be controlled using prompts with user specified control tokens.

传达相关和忠实信息的能力对于有条件生成的许多任务至关重要，但对于神经SEQ-seq seq模型仍然难以捉摸，这些模型的输出通常显示出幻觉，并且无法正确涵盖重要细节。在这项工作中，我们主张规划作为有用的中间表示，以使有条件的一代减少不透明和扎根。我们的作品提出了将文本计划作为一系列提问（QA）对的新概念化。我们用QA蓝图作为内容选择（即〜说什么）和计划（即〜按什么顺序）来增强现有数据集（例如，用于摘要）。我们通过利用最先进的问题生成技术并将输入输出对自动获取蓝图，并将其转换为输入 - 蓝图输出输出元组。我们开发了基于变压器的模型，每个模型都在它们如何将蓝图合并到生成的输出中（例如，作为全局计划或迭代）。跨指标和数据集的评估表明，蓝图模型比不采取计划并允许对生成输出进行更严格控制的替代方案更为事实。

当前的抽象摘要模型要么仅通过突出源文档的一部分而缺乏明显的解释性或提供不完整的理由。为此，我们提出了摘要程序（SP），这是一个由二进制树的（有序）列表组成的可解释的模块化框架，每个框架都编码来自源文档的抽象摘要句子的分步生成过程。一个摘要程序每个摘要句子包含一个根节点，一棵不同的树将每个摘要句子（根节点）连接到派生的文档句子（叶节点），其中包含中间生成的句子的连接节点。边缘代表涉及摘要的不同模块化操作，例如句子融合，压缩和释义。我们首先建议通过神经模块提出有效的最佳搜索方法，SP搜索通过直接优化Rouge分数来识别人类摘要的SP搜索。接下来，使用这些程序作为自动监督，我们建议使用生成摘要程序的SEQ2SEQ模型，然后执行以获取最终摘要。我们证明，SP搜索有效地代表了使用通常忠于其预期行为的模块的人类摘要背后的生成过程。我们还进行了一项仿真研究，以表明汇总计划通过允许人类更好地模拟模型推理来改善摘要模型的解释性。汇总计划构成了朝着可解释和模块化的抽象摘要迈出的有希望的步骤，这是先前主要通过黑框端到端神经系统解决的复杂任务。我们的代码可从https://github.com/swarnahub/summarization Programs获得

Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation model reflect its preferences for the generated summary, including the preference for factual consistency, and the preference for the language or knowledge prior as well. To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt. More specifically, the framework performs an extra inference step in which a text prompt is introduced as an additional input. In this way, another preference is described by the generation probability of this extra inference process. The difference between the above two preferences, i.e. the difference between the probabilities, could be used as measurements for detecting factual inconsistencies. Interestingly, we found that with the properly designed prompt, our framework could evaluate specific preferences and serve as measurements for fine-grained categories of inconsistency, such as entity-related inconsistency, coreference-related inconsistency, etc. Moreover, our framework could also be extended to the supervised setting to learn better prompt from the labeled data as well. Experiments show that our framework achieves new SOTA results on three factual inconsistency detection tasks.