A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SUMREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.

索赔检测和验证对于新闻认识至关重要，并且已成为有前途的技术，以减轻新闻中的错误信息。然而，大多数现有的工作侧重于索赔句子的分析，同时俯瞰关键背景属性，例如索引者，声称对象和连接到索赔的其他知识。在这项工作中，我们提供了新闻本，新的基准，了解新闻领域的知识意识索赔检测。我们重新定义了索赔探测问题，包括提取与索赔相关的附加背景属性，并发布529索赔由103个新闻文章提示。此外，报讯人旨在在新兴场景中索取索赔检测系统，包括不少培训数据的看不见的主题。最后，我们对这款新基准测试提供了对各种零射和及时的基础基准的全面评估。

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。

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。

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.

随着近期自然语言生成（NLG）模型的各种应用程序的改进，它变得必须具有识别和评估NLG输出是否仅共享关于外部世界的可验证信息的手段。在这项工作中，我们提出了一个归属于识别的来源（AIS）的新评估框架，用于评估自然语言生成模型的输出，当这种输出涉及外部世界时。我们首先定义AIS，并引入两级注释管道，用于允许注释器根据AIS指南适当地评估模型输出。通过人为评估研究，我们在三个代数据集（会话QA域中的两个中和总结一下，概括地验证了这种方法，表明AIS可以作为测量模型生成的语句是否支持基础来源的常见框架。我们释放人类评估研究指南。

随着大型语言模型的出现，抽象性摘要的方法取得了长足的进步，从而在应用程序中使用了帮助知识工人处理笨拙的文档收集的潜力。一个这样的环境是民权诉讼交换所（CRLC）（https://clearinghouse.net），其中发布了有关大规模民权诉讼，服务律师，学者和公众的信息。如今，CRLC中的摘要需要对律师和法律专业的学生进行广泛的培训，这些律师和法律专业的学生花费数小时了解多个相关文件，以便产生重要事件和结果的高质量摘要。在这种持续的现实世界摘要工作的激励下，我们引入了Multi-iplesum，这是由正在进行的CRLC写作中绘制的9,280个专家作者的摘要集。鉴于源文档的长度，多文章介绍了一个具有挑战性的多文档摘要任务，通常每个情况超过200页。此外，多胎sum与其多个目标摘要中的其他数据集不同，每个数据集都处于不同的粒度（从一句“极端”摘要到超过五百个单词的多段落叙述）。我们提供了广泛的分析，表明，尽管培训数据（遵守严格的内容和样式准则）中的摘要很高，但最新的摘要模型在此任务上的表现较差。我们发布了多体式的摘要方法，以及促进应用程序的开发，以协助CRLC的任务https://multilexsum.github.io。

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.

在典型的客户服务聊天方案中，客户联系支持中心以便帮助或提高投诉，人类代理商试图解决这些问题。在大多数情况下，在谈话结束时，要求代理人写一份简短的总结强调问题和建议的解决方案，通常是为了使其他可能需要处理同一客户或问题的其他代理商的利益。本文的目标是推进此任务的自动化。我们介绍了第一个大规模，高质量的客户服务对话框摘要数据集，接近6500人的注释摘要。数据基于现实世界的客户支持对话框，包括提取和抽象摘要。我们还介绍了一种特定于对话框的新无监督的提取摘要方法。

The diverse demands of different summarization tasks and their high annotation costs are driving a need for few-shot summarization. However, despite the emergence of many summarization tasks and datasets, the current training paradigm for few-shot summarization systems ignores potentially shareable knowledge in heterogeneous datasets. To this end, we propose \textsc{UniSumm}, a unified few-shot summarization model pre-trained with multiple summarization tasks and can be prefix-tuned to excel at any few-shot summarization datasets. Meanwhile, to better evaluate few-shot summarization systems, under the principles of diversity and robustness, we assemble and publicize a new benchmark \textsc{SummZoo}. It consists of $8$ diverse summarization tasks with multiple sets of few-shot samples for each task, covering both monologue and dialogue domains. Experimental results and ablation studies show that \textsc{UniSumm} outperforms strong baseline systems by a large margin across all tasks in \textsc{SummZoo} under both automatic and human evaluations. We release our code and benchmark at \url{https://github.com/microsoft/UniSumm}.

我们介绍了微博观点摘要（MOS）的任务，并共享3100个金标准意见摘要的数据集，以促进该领域的研究。该数据集包含跨越2年期的推文的摘要，并且涵盖了比任何其他公共Twitter摘要数据集更多的主题。摘要本质上是抽象的，是由熟练的记者创建的，这些记者在将事实信息（主要故事）与作者观点分开的模板之后，总结了新闻文章。我们的方法不同于以前在社交媒体中生成金标准摘要的工作，这些摘要通常涉及选择代表性帖子，从而有利于提取性摘要模型。为了展示数据集的实用性和挑战，我们基准了一系列抽象性和提取性的最先进的摘要模型，并实现良好的性能，前者的表现优于后者。我们还表明，微调对于提高性能和研究使用不同样本量的好处是必要的。

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.

Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.

Very large language models such as GPT-3 have shown impressive performance across a wide variety of tasks, including text summarization. In this paper, we show that this strong performance extends to opinion summarization. We explore several pipeline methods for applying GPT-3 to summarize a large collection of user reviews in a zero-shot fashion, notably approaches based on recursive summarization and selecting salient content to summarize through supervised clustering or extraction. On two datasets, an aspect-oriented summarization dataset of hotel reviews and a generic summarization dataset of Amazon and Yelp reviews, we show that the GPT-3 models achieve very strong performance in human evaluation. We argue that standard evaluation metrics do not reflect this, and evaluate against several new measures targeting faithfulness, factuality, and genericity to contrast these different methods.

Information overloading requires the need for summarizers to extract salient information from the text. Currently, there is an overload of dialogue data due to the rise of virtual communication platforms. The rise of Covid-19 has led people to rely on online communication platforms like Zoom, Slack, Microsoft Teams, Discord, etc. to conduct their company meetings. Instead of going through the entire meeting transcripts, people can use meeting summarizers to select useful data. Nevertheless, there is a lack of comprehensive surveys in the field of meeting summarizers. In this survey, we aim to cover recent meeting summarization techniques. Our survey offers a general overview of text summarization along with datasets and evaluation metrics for meeting summarization. We also provide the performance of each summarizer on a leaderboard. We conclude our survey with different challenges in this domain and potential research opportunities for future researchers.

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。

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.

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.

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。