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

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

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-toword structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.

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.

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. 1 Compared to recurrent models, computations over all elements can be fully parallelized during training to better exploit the GPU hardware and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.

文件摘要提供了一种更快地了解文本文档的集合并具有几个现实生活应用程序的仪器。随着在线文本数据的增长，最近提出了许多摘要模型。基于序列的序列（SEQ2Seq）的神经摘要模型是由于其高性能而在摘要领域中最广泛使用的。这是因为在编码时，文本中的语义信息和结构信息被充分考虑。然而，现有的提取摘要模型几乎没有注意并使用中央主题信息来协助生成摘要，这导致模型不确保在主要主题下的生成摘要。冗长的文档可以跨越多个主题，单个摘要无法对所有主题进行正义。因此，生成高质量摘要的关键是基于它的核心主题和构建摘要，特别是对于长文档来说。我们提出了一个主题感知编码，用于处理此问题的文档摘要。该模型有效地结合了句法级和主题级信息来构建一个综合句子表示。具体地，在神经基句子级表示中添加了神经主题模型，学习以充分考虑用于捕获原始文档中的关键内容的中心主题信息。三个公共数据集的实验结果表明，我们的模型优于最先进的模型。

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。

尽管具有抽象文本摘要的神经序列到序列模型的成功，但它具有一些缺点，例如重复不准确的事实细节并倾向于重复自己。我们提出了一个混合指针发生器网络，以解决再现事实细节的缺点和短语重复。我们使用混合指针发生器网络增强了基于注意的序列到序列，该混合指针发生器网络可以生成词汇单词并增强再现真实细节的准确性和劝阻重复的覆盖机制。它产生合理的输出文本，可以保留输入文章的概念完整性和事实信息。为了评估，我们主要雇用“百拉那” - 一个高度采用的公共孟加拉数据集。此外，我们准备了一个名为“BANS-133”的大型数据集，由133K Bangla新闻文章组成，与人类生成的摘要相关。试验拟议的模型，我们分别实现了胭脂-1和胭脂 - 2分别为0.66,0.41的“Bansdata”数据集，分别为0.67,0.42，为Bans-133k“数据集。我们证明了所提出的系统超过以前的国家 - 近距离数据集的近距离攀义概要技术及其稳定性。“Bans-133”数据集和代码基础将公开进行研究。

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.

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。

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。

维基百科是可理解知识的重要自由来源。尽管如此，巴西葡萄牙维基百科仍然缺乏对许多科目的描述。为了扩大巴西维基百科，我们贡献了Plsum，这是一种从多个描述性网站生成类似的Wiki的抽象摘要的框架。该框架具有提取阶段，然后是抽象。特别是，对于抽象阶段，我们微调并比较了变压器神经网络，PTT5和啰覆的最近最近的变化。为了微调和评估模型，我们创建了一个具有数千个示例的数据集，将参考网站链接到维基百科。我们的结果表明，可以从巴西葡萄牙语网上内容生成有意义的抽象摘要。

多文件摘要（MDS）是信息聚合的有效工具，它从与主题相关文档集群生成信息和简洁的摘要。我们的调查是，首先，系统地概述了最近的基于深度学习的MDS模型。我们提出了一种新的分类学，总结神经网络的设计策略，并进行全面的最先进的概要。我们突出了在现有文献中很少讨论的各种客观函数之间的差异。最后，我们提出了与这个新的和令人兴奋的领域有关的几个方向。

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.

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。

Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case summarization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.

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.

学术研究是解决以前从未解决过的问题的探索活动。通过这种性质，每个学术研究工作都需要进行文献审查，以区分其Novelties尚未通过事先作品解决。在自然语言处理中，该文献综述通常在“相关工作”部分下进行。鉴于研究文件的其余部分和引用的论文列表，自动相关工作生成的任务旨在自动生成“相关工作”部分。虽然这项任务是在10年前提出的，但直到最近，它被认为是作为科学多文件摘要问题的变种。然而，即使在今天，尚未标准化了自动相关工作和引用文本生成的问题。在这项调查中，我们进行了一个元研究，从问题制定，数据集收集，方法方法，绩效评估和未来前景的角度来比较相关工作的现有文献，以便为读者洞察到国家的进步 - 最内容的研究，以及如何进行未来的研究。我们还调查了我们建议未来工作要考虑整合的相关研究领域。

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。

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。