现有的抽象摘要模型缺乏明确的控制机制，允许用户影响模型输出的风格特征。这导致生成不迎合用户需求或偏好的通用摘要。为了解决这个问题，我们介绍了Hydrasum，这是一种新的摘要架构，其扩展了当前模型的单个解码器框架，例如， BART，到专家的混合版本，包括多个解码器。我们拟议的模型鼓励每个专家，即解码器，沿着尺寸学习和生成风格不同的摘要，例如抽象，长度，特异性等。在每个时间步骤中，Hydrasum采用一个门控机制，该机构决定每个单独解码器对下一个令牌的输出概率分布的贡献。通过对三个摘要数据集的实验（CNN，新闻编辑室，XSUM），我们证明了这种门控机制自动学习在标准培训目标下将对比摘要样式分配给不同的水路解码器，而无需额外监督。我们进一步表明，培训过程的指导版本可以明确地管理哪些摘要样式在解码器之间分区，例如，高抽象力与低吸引力或高特异性与低特异性，并且还增加各个解码器之间的致命差异。最后，我们的实验表明，我们的解码器框架非常灵活：在推理期间，我们可以从单独的解码器或解码器的不同子集的混合物中进行采样，以产生多种摘要，并强制对摘要生成的单一和多样式控制。

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.

We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question "What is the article about?". We collect a real-world, large scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures longrange dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. 1

GPT-3等模型的零和少量提示的最新成功导致了NLP研究的范式转移。在本文中，我们研究了其对文本摘要的影响，重点是新闻摘要的经典基准领域。首先，我们研究了零击GPT-3与在大型摘要数据集中训练的微调模型的比较。我们表明，不仅人类压倒性地更喜欢GPT-3摘要，而且这些摘要也不遭受普通数据集特异性问题（例如事实差的问题）。接下来，我们研究这对评估意味着什么，尤其是黄金标准测试集的作用。我们的实验表明，基于参考和无参考的自动指标，例如最近提出的基于质量检查或基于质量的事实方法无法可靠地评估零击摘要。最后，我们讨论了未来的研究挑战，除了通用摘要之外，特别是基于关键字和方面的摘要，表明了优势微调方法与零拍的提示相比如何。为了支持进一步的研究，我们发布：（a）在4个标准摘要基准中，从微调和零摄像模型中产生的10K生成的摘要，（b）1K人类偏好判断和比较不同系统的普通系统，以进行通用和关键字的不同系统。基于摘要。

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

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

具有复制机制的最近神经序列到序列模型在各种文本生成任务中取得了显着的进展。这些模型解决了词汇问题，并促进了稀有词的产生。然而，如先前的复制模型所观察到的，难以产生的，难以产生和缺乏抽象，难以识别。在本文中，我们提出了一种副本网络的新颖监督方法，该方法可帮助模型决定需要复制哪些单词并需要生成。具体而言，我们重新定义目标函数，它利用源序列和目标词汇表作为复制的指导。关于数据到文本生成和抽象总结任务的实验结果验证了我们的方法提高了复制质量，提高了抽象程度。

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。

查询聚焦的文本摘要（QFTS）任务旨在构建基于给定查询的文本文档摘要的构建系统。解决此任务的关键挑战是缺乏培训摘要模型的大量标记数据。在本文中，我们通过探索一系列域适应技术来解决这一挑战。鉴于最近在广泛的自然语言处理任务中进行预先接受的变压器模型的成功，我们利用此类模型为单文档和多文件方案的QFTS任务产生抽象摘要。对于域适应，我们使用预先训练的变压器的摘要模型应用了各种技术，包括转移学习，弱监督学习和远程监督。六个数据集的广泛实验表明，我们所提出的方法非常有效地为QFTS任务产生抽象摘要，同时在一组自动和人类评估指标上设置新的最先进的结果。

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.

Current state-of-the-art summarization models are trained with either maximum likelihood estimation (MLE) or reinforcement learning (RL). In this study, we investigate the third training paradigm and argue that inverse reinforcement learning (IRL) may be more suitable for text summarization. IRL focuses on estimating the reward function of an agent, given a set of observations of that agent's behavior. Generally, IRL provides advantages in situations where the reward function is not explicitly known or where it is difficult to define or interact with the environment directly. These situations are exactly what we observe in summarization. Thus, we introduce inverse reinforcement learning into text summarization and define a suite of sub-rewards that are important for summarization optimization. By simultaneously estimating the reward function and optimizing the summarization agent with expert demonstrations, we show that the model trained with IRL produces summaries that closely follow human behavior, in terms of better ROUGE, coverage, novelty, compression ratio and factuality when compared to the baselines trained with MLE and RL.

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。

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。

With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models whereas only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the pre-trained unsupervised PEGASUS by 4.37% to 7.27% relative mean ROUGE across four widely-adopted summarization benchmarks, and achieves relative gains of 7.51% (up to 23.73%) averaged over 30 transfer setups.

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。

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

主题控制的摘要是一个具有广泛潜在应用的新兴研究领域。但是，现有方法受到重大局限性。首先，目前尚无针对此任务的确定评估指标。此外，现有的方法基于经常性架构，与最新的基于变压器的架构相比，这可能会大大限制其性能，同时它们还需要对模型的架构进行修改以控制主题。在这项工作中，我们提出了一种新的面向主题的评估措施，以根据生成的摘要与所需主题之间的主题亲和力自动评估生成的摘要。我们还进行了一项用户研究，以验证该措施的可靠性。最后，我们提出了简单而有力的方法，用于将主题控制的摘要要么将主题嵌入到模型的体系结构中，要么采用控制令牌来指导摘要生成。实验结果表明，与更复杂的基于嵌入的方法相比，对照令牌可以实现更好的性能，同时更快。

Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing works have to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on all metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.

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。

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