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。

自动医疗问题摘要可以极大地帮助系统了解消费者健康问题并检索正确的答案。基于最大似然估计（MLE）的SEQ2SEQ模型已在此任务中应用，这面临两个一般问题：该模型无法捕获良好的问题，并且传统的MLE策略缺乏理解句子级语义的能力。为了减轻这些问题，我们提出了一个新颖的问题焦点驱动的对比学习框架（QFCL）。特别是，我们提出了一种简单有效的方法来基于问题的重点生成硬性样本，并利用编码器和解码器的对比度学习以获得更好的句子级别表示。在三个医疗基准数据集上，我们提出的模型可实现新的最新结果，并在三个数据集的基线BART模型上获得了5.33、12.85和3.81点的性能增益。进一步的人类判断和详细的分析证明，我们的QFCL模型可以学习更好的句子表示，具有区分不同句子含义的能力，并通过捕获问题重点来产生高质量的摘要。

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.

对比学习模型在无监督的视觉表示学习中取得了巨大成功，这使得相同图像的不同视图的特征表示之间的相似性最大化，同时最小化不同图像的视图的特征表示之间的相似性。在文本摘要中，输出摘要是输入文档的较短形式，它们具有类似的含义。在本文中，我们提出了对监督抽象文本摘要的对比学习模型，在那里我们查看文档，它的金摘要及其模型生成的摘要，与相同的平均表示的不同视图，并在培训期间最大化它们之间的相似性。我们在三个不同的摘要数据集上改进了一个强序列到序列文本生成模型（即，BART）。人类评估还表明，与其对应物相比，我们的模型达到了更好的忠实性评级，没有对比的目标。

用于提取和抽象性摘要系统的传统培训范例始终仅使用令牌级别或句子级培训目标。但是，始终从摘要级别评估输出摘要，从而导致培训和评估的不一致。在本文中，我们提出了一个基于对比度学习的重新排列框架，用于一阶段的摘要，称为COLO。通过建模对比目标，我们表明摘要模型能够根据摘要级别的分数直接生成摘要，而无需其他模块和参数。广泛的实验表明，CORO在CNN/DailyMail基准测试中提高了单阶段系统的提取和抽象结果，将其提高到44.58和46.33 Rouge-1得分，同时保留了参数效率和推断效率。与最先进的多阶段系统相比，我们节省了100多个GPU训练时间，并在推理期间获得3〜8加速比，同时保持可比的结果。

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.

Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting their real-world usage. We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages. To address this task, we introduce a new dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we propose an automatic metric, MAFE, for factual correctness of generated descriptions. Our EntDescriptor model is equipped with strong rankers to fetch helpful passages and generate entity descriptions. Experimental result shows a good correlation (60.14) between our proposed metric and human judgments of factuality. Our rankers significantly improved the factual correctness of generated descriptions (15.95% and 34.51% relative gains in recall and precision). Finally, our ablation study highlights the benefit of combining keys and groundings.

Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive learning in neural text generation usually lead to inferior performance. In this paper, we analyse the underlying reasons and propose a new Contrastive Neural Text generation framework, CoNT. CoNT addresses bottlenecks that prevent contrastive learning from being widely adopted in generation tasks from three aspects -- the construction of contrastive examples, the choice of the contrastive loss, and the strategy in decoding. We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation. Experimental results show that CoNT clearly outperforms the conventional training framework on all the ten benchmarks with a convincing margin. Especially, CoNT surpasses previous the most competitive contrastive learning method for text generation, by 1.50 BLEU on machine translation and 1.77 ROUGE-1 on summarization, respectively. It achieves new state-of-the-art on summarization, code comment generation (without external data) and data-to-text generation.

现有摘要系统主要生成纯粹依赖源文档内容的摘要。但是，即使对于人类，我们通常需要一些引用或示例，帮助我们充分了解源文档并以特定格式写入摘要。但是如何找到高质量的样式，并将它们纳入总结系统仍然挑战和探索。在本文中，我们提出了一种由致密的猎犬和摘要提升的新型检索增强的抽象概要框架。首先，检索几个密切相关的示例作为补充输入，以帮助生成模型更全面地了解文本。此外，检索的示例也可以在引导模型以捕获特定语料库的写入风格中起作用。我们在多个域和两个骨干型号的各种摘要数据集上验证我们的方法：BERT和BART。结果表明，与强大的预训练模型相比，我们的框架在胭脂-1分数中获得了1.38〜4.66的显着改善，并在账单上实现了新的最先进。人类评估表明我们的检索增强模型可以更好地捕获特定于域的书写风格。

文本摘要的重写方法结合了提取性和抽象的方法，使用抽象模型提高了提取性摘要的简洁性和可读性。退出重写系统将每个提取性句子作为唯一的输入，它相对集中，但可能会失去必要的背景知识和话语上下文。在本文中，我们调查了上下文化的重写，该重写消耗了整个文档并考虑了摘要上下文。我们将上下文重写正式化为具有组标签对齐的SEQ2SEQ，将组标签引入了模拟对齐方式的解决方案，并通过基于内容的地址来识别提取句子。结果表明，我们的方法显着优于非上下文重写系统，而无需加强学习，从而在多个提取器上实现了胭脂分数的强烈改进。

在本文中，我们建议利用对话的独特特征，共享参与者的常识性知识，以解决总结它们的困难。我们提出了病态的框架，该框架使用常识推论作为其他背景。与以前仅依赖于输入对话的工作相比，Sick使用外部知识模型来生成丰富的常识推断，并选择具有基于相似性选择方法的最可能的推理。基于生病的，病人++的理解为监督，在总结多任务学习环境中的对话时，添加了产生常识推断的任务。实验结果表明，通过注入常识性知识，我们的框架比现有方法产生更多信息和一致的摘要。

ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model that optimizes at a unigram level. We present differentiable n-gram objectives, attempting to alleviate the discrepancy between training criterion and evaluating criterion. The objective maximizes the probabilistic weight of matched sub-sequences, and the novelty of our work is the objective weights the matched sub-sequences equally and does not ceil the number of matched sub-sequences by the ground truth count of n-grams in reference sequence. We jointly optimize cross-entropy loss and the proposed objective, providing decent ROUGE score enhancement over abstractive summarization dataset CNN/DM and XSum, outperforming alternative n-gram objectives.

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.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

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。

本文对过去二十年来对自然语言生成（NLG）的研究提供了全面的审查，特别是与数据到文本生成和文本到文本生成深度学习方法有关，以及NLG的新应用技术。该调查旨在（a）给出关于NLG核心任务的最新综合，以及该领域采用的建筑;（b）详细介绍各种NLG任务和数据集，并提请注意NLG评估中的挑战，专注于不同的评估方法及其关系;（c）强调一些未来的强调和相对近期的研究问题，因为NLG和其他人工智能领域的协同作用而增加，例如计算机视觉，文本和计算创造力。

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。

诸如学术文章和商业报告之类的长期文件一直是详细说明重要问题和需要额外关注的复杂主题的标准格式。自动汇总系统可以有效地将长文档置于简短而简洁的文本中，以封装最重要的信息，从而在帮助读者的理解中很重要。最近，随着神经体系结构的出现，已经做出了重大的研究工作，以推动自动文本摘要系统，以及有关将这些系统扩展到长期文档领域的挑战的大量研究。在这项调查中，我们提供了有关长期文档摘要的研究的全面概述，以及其研究环境的三个主要组成部分的系统评估：基准数据集，汇总模型和评估指标。对于每个组成部分，我们在长期汇总的背景下组织文献，并进行经验分析，以扩大有关当前研究进度的观点。实证分析包括一项研究基准数据集的内在特征，摘要模型的多维分析以及摘要评估指标的综述。根据总体发现，我们通过提出可能在这个快速增长的领域中提出未来探索的方向来得出结论。

三重提取是自然语言处理和知识图构建信息提取的重要任务。在本文中，我们重新审视了序列生成的端到端三重提取任务。由于生成三重提取可能难以捕获长期依赖性并产生不忠的三元组，因此我们引入了一种新型模型，即与生成变压器的对比度三重提取。具体而言，我们为基于编码器的生成引入了一个共享的变压器模块。为了产生忠实的结果，我们提出了一个新颖的三胞胎对比训练对象。此外，我们引入了两种机制，以进一步提高模型性能（即，批处理动态注意力掩盖和三个方面的校准）。在三个数据集（即NYT，WebNLG和MIE）上进行的实验结果表明，我们的方法比基线的方法更好。

Bidirectional Encoder Representations from Transformers (BERT; Devlin et al. 2019) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several intersentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves stateof-the-art results across the board in both extractive and abstractive settings. 1