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。

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

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。

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.

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。

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。

与单案摘要相比，抽象性多文件摘要（MDS）对其冗长和链接的来源的表示和覆盖范围提出了挑战。这项研究开发了一个平行的层次变压器（PHT），具有MDS的注意对齐。通过合并单词和段落级的多头注意，PHT的层次结构可以更好地处理令牌和文档级别的依赖项。为了指导解码到更好的源文档覆盖范围，然后将注意力调整机制引入以校准光束搜索，并预测的最佳注意力分布。根据Wikisum数据，进行了全面的评估，以测试拟议的体系结构对MD的改进。通过更好地处理内部和跨文档的信息，结果胭脂和人类评估都表明，我们的分层模型以相对较低的计算成本生成较高质量的摘要。

由于免费的在线百科全书具有大量内容，因此Wikipedia和Wikidata是许多自然语言处理（NLP）任务的关键，例如信息检索，知识基础构建，机器翻译，文本分类和文本摘要。在本文中，我们介绍了Wikides，这是一个新颖的数据集，用于为文本摘要问题提供Wikipedia文章的简短描述。该数据集由6987个主题上的80K英语样本组成。我们设置了一种两阶段的摘要方法 - 描述生成（I阶段）和候选排名（II阶段）作为一种依赖于转移和对比学习的强大方法。对于描述生成，与其他小规模的预训练模型相比，T5和BART表现出了优越性。通过将对比度学习与Beam Search的不同输入一起应用，基于度量的排名模型优于直接描述生成模型，在主题独立拆分和独立于主题的独立拆分中，最高可达22个胭脂。此外，第II期中的结果描述得到了人类评估的支持，其中45.33％以上，而I阶段的23.66％则支持针对黄金描述。在情感分析方面，生成的描述无法有效地从段落中捕获所有情感极性，同时从黄金描述中更好地完成此任务。自动产生的新描述减少了人类为创建它们的努力，并丰富了基于Wikidata的知识图。我们的论文对Wikipedia和Wikidata产生了实际影响，因为有成千上万的描述。最后，我们预计Wikides将成为从短段落中捕获显着信息的相关作品的有用数据集。策划的数据集可公开可用：https：//github.com/declare-lab/wikides。

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by ( 1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new stateof-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.

多文件科学摘要（MDSS）旨在为与主题相关的科学论文群生成连贯和简洁的摘要。此任务需要精确理解纸张内容以及对交叉纸关系的准确建模。知识图为文档传达了紧凑且可解释的结构化信息，这使其非常适合内容建模和关系建模。在本文中，我们提出了KGSUM，这是一个MDSS模型，以编码和解码过程中的知识图为中心。具体而言，在编码过程中，提出了两个基于图的模块，以将知识图信息纳入纸张编码，而在解码过程中，我们通过以描述性句子的形式首先生成摘要的知识图，提出了一个两阶段解码器。 ，然后生成最终摘要。经验结果表明，所提出的体系结构对多XSCIENCE数据集的基准进行了实质性改进。

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

现有以查询为中心的摘要数据集的大小有限，使培训数据驱动的摘要模型提出了挑战。同时，以查询为重点的摘要语料库的手动构造昂贵且耗时。在本文中，我们使用Wikipedia自动收集超过280，000个示例的大型以查询为中心的摘要数据集（名为Wikiref），这可以用作数据增强的手段。我们还开发了一个基于BERT的以查询为重点的摘要模型（Q-bert），以从文档中提取句子作为摘要。为了更好地调整包含数百万个参数的巨大模型，我们仅识别和微调一个稀疏的子网络，这对应于整个模型参数的一小部分。三个DUC基准测试的实验结果表明，在Wikiref中预先培训的模型已经达到了合理的性能。在对特定基准数据集进行了微调后，具有数据增强的模型优于强大比较系统。此外，我们提出的Q-Bert模型和子网微调都进一步改善了模型性能。该数据集可在https://aka.ms/wikiref上公开获取。

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.

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。

The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually trained by maximizing the likelihood the output text sequence and assumes the input sequence and all gold preceding tokens are given during training, while during inference the model suffers from the exposure bias problem (i.e., it only has access to its previously predicted tokens rather gold tokens during beam search). In this paper, we propose MoCa ({\bf Mo}mentum {\bf Ca}libration) for text generation. MoCa is an online method that dynamically generates slowly evolving (but consistent) samples using a momentum moving average generator with beam search and MoCa learns to align its model scores of these samples with their actual qualities. Experiments on four text generation datasets (i.e., CNN/DailyMail, XSum, SAMSum and Gigaword) show MoCa consistently improves strong pre-trained transformers using vanilla fine-tuning and we achieve the state-of-the-art results on CNN/DailyMail and SAMSum datasets.

The research on text summarization for low-resource Indian languages has been limited due to the availability of relevant datasets. This paper presents a summary of various deep-learning approaches used for the ILSUM 2022 Indic language summarization datasets. The ISUM 2022 dataset consists of news articles written in Indian English, Hindi, and Gujarati respectively, and their ground-truth summarizations. In our work, we explore different pre-trained seq2seq models and fine-tune those with the ILSUM 2022 datasets. In our case, the fine-tuned SoTA PEGASUS model worked the best for English, the fine-tuned IndicBART model with augmented data for Hindi, and again fine-tuned PEGASUS model along with a translation mapping-based approach for Gujarati. Our scores on the obtained inferences were evaluated using ROUGE-1, ROUGE-2, and ROUGE-4 as the evaluation metrics.

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

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。

通过言语技术的最新进步和智能助理的引入，如亚马逊Alexa，Apple Siri和Google Home，越来越多的用户通过语音命令与各种应用程序进行交互。电子商务公司通常在其网页上显示较短的产品标题，在需要简洁时，可以在其网页上进行人工策划或算法生成。然而，这些标题与自然语言不同。例如，“幸运的魅力面筋无麸质谷物，20.5盎司盒装幸运魅力含有无麸质”可以在网页上显示，而在基于语音的文本到语音应用程序中不能使用类似的标题。在这种对话系统中，易于理解的句子，例如“20.5盎司的幸运魅力麸质谷物”是优选的。与显示设备相比，可以向用户呈现图像和详细的产品信息，在与语音助手相互作用时，需要传达最重要信息的产品的短标题。我们提出Ebert，通过进一步预先训练电子商务产品描述语料库中的BERT嵌入来进行序列到序列方法，然后微调结果模型，以产生来自输入Web标题的短，自然的语言标题。我们对现实世界行业数据集的广泛实验，以及对模型输出的人类评估，表明Ebert摘要优于相当的基线模型。由于该模型的功效，该模型的版本已在真实世界中进行部署。

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。