机器生成的引文句可以帮助自动化的科学文献综述和协助文章写作。生成引用文本的当前方法仅限于使用引用文档和引用文档作为输入的单引用生成。然而，在现实世界的情况下，作家往往总结一句话中的几个研究或讨论整个段落的相关信息。此外，先前已经确定了多种引用意图，这意味着作者可能需要控制产生的句子的意图，以涵盖不同的场景。因此，这项工作侧重于生成多个引用并释放名为CITEMI的新收集的数据集来推动未来的研究。我们首先使用融合的解码器方法构建一个新的一代模型来应对多个长输入。其次，我们将预测的引用意图纳入意图控制的培训。实验表明，拟议的方法提供了更加全面的功能，以产生引用句子。

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。

The publication rates are skyrocketing across many fields of science, and it is difficult to stay up to date with the latest research. This makes automatically summarizing the latest findings and helping scholars to synthesize related work in a given area an attractive research objective. In this paper we study the problem of citation text generation, where given a set of cited papers and citing context the model should generate a citation text. While citation text generation has been tackled in prior work, existing studies use different datasets and task definitions, which makes it hard to study citation text generation systematically. To address this, we propose CiteBench: a benchmark for citation text generation that unifies the previous datasets and enables standardized evaluation of citation text generation models across task settings and domains. Using the new benchmark, we investigate the performance of multiple strong baselines, test their transferability between the datasets, and deliver new insights into task definition and evaluation to guide the future research in citation text generation. We make CiteBench publicly available at https://github.com/UKPLab/citebench.

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。

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。

直接使用现有的文本生成数据集进行可控生成时，我们面临的问题是没有域知识，因此可以控制的方面受到限制。一个典型的示例是，当使用CNN/Daily Mail数据集用于可控文本摘要时，没有关于摘要句子的重点的指导信息。更有用的文本生成器应利用输入文本和控制信号来指导生成，只能在对域知识的深入了解中构建。在这个愿景的激励下，我们的论文介绍了一个名为Mred的新文本生成数据集。我们的新数据集由7,089个元评论组成，其所有45k元评论句子都用9个精心定义的类别之一手动注释，包括抽象，力量，决策等。我们介绍了对开始的实验结果摘要模型，并提出了使用我们的带注释数据的方法对结构控制生成的方法。通过探索各种设置并分析模型行为相对于控制信号，我们证明了我们提出的任务的挑战以及数据集MRD的值。同时，MRD还使我们能够更好地了解元评论域。

Multi-document summarization (MDS) has traditionally been studied assuming a set of ground-truth topic-related input documents is provided. In practice, the input document set is unlikely to be available a priori and would need to be retrieved based on an information need, a setting we call open-domain MDS. We experiment with current state-of-the-art retrieval and summarization models on several popular MDS datasets extended to the open-domain setting. We find that existing summarizers suffer large reductions in performance when applied as-is to this more realistic task, though training summarizers with retrieved inputs can reduce their sensitivity retrieval errors. To further probe these findings, we conduct perturbation experiments on summarizer inputs to study the impact of different types of document retrieval errors. Based on our results, we provide practical guidelines to help facilitate a shift to open-domain MDS. We release our code and experimental results alongside all data or model artifacts created during our investigation.

The task of automatic text summarization produces a concise and fluent text summary while preserving key information and overall meaning. Recent approaches to document-level summarization have seen significant improvements in recent years by using models based on the Transformer architecture. However, the quadratic memory and time complexities with respect to the sequence length make them very expensive to use, especially with long sequences, as required by document-level summarization. Our work addresses the problem of document-level summarization by studying how efficient Transformer techniques can be used to improve the automatic summarization of very long texts. In particular, we will use the arXiv dataset, consisting of several scientific papers and the corresponding abstracts, as baselines for this work. Then, we propose a novel retrieval-enhanced approach based on the architecture which reduces the cost of generating a summary of the entire document by processing smaller chunks. The results were below the baselines but suggest a more efficient memory a consumption and truthfulness.

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

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.

健康素养被出现为制定适当的健康决策和确保治疗结果的关键因素。然而，医学术语和该领域的专业语言的复杂结构使健康信息尤为难以解释。因此，迫切需要对自动化方法来提高生物医学文献的可访问性，以提高一般人群。这个问题可以作为医疗保健专业人员语言与公众的语言之间的翻译问题。在本文中，我们介绍了自动化生物医学科学评论的制定语言摘要的新任务，建设了一个数据集，以支持自动化方法的开发和评估，以提高生物医学文献的可访问性。我们对解决这项任务的各种挑战进行了分析，包括不仅对关键要点的总结，而且还概述了对背景知识和专业语言的简化的解释。我们试验最先进的摘要模型以及多种数据增强技术，并使用自动指标和人工评估评估其性能。结果表明，与专家专家专门开发的参考摘要相比，使用当代神经架构产生的自动产生的摘要可以实现有希望的质量和可读性（最佳Rouge-L为50.24和Flesch-Kincaid可读性得分为13.30）。我们还讨论了目前尝试的局限性，为未来工作提供了洞察和方向。

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents for a given query. We introduce the first large-scale collection of approximately 40 retrieval datasets with instructions, BERRI, and present TART, a multi-task retrieval system trained on BERRI with instructions. TART shows strong capabilities to adapt to a new retrieval task via instructions and advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval to better reflect real-world scenarios, where diverse domains and tasks are pooled and a system needs to find documents aligning users' intents. In this setup, TART significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.

例如，查询是一个众所周知的信息检索任务，其中由用户选择文档作为搜索查询，目标是从大集合中检索相关文档。但是，文档通常涵盖主题的多个方面。要解决此方案，我们将通过示例介绍面位查询的任务，其中用户还可以指定除输入查询文档之外的更精细的粗体方面。我们专注于在科学文献搜索中的应用。我们设想能够沿着专门选择的修辞结构元素作为对此问题的一种解决方案来检索类似于查询科学纸的科学论文。在这项工作中，我们称之为方面的修辞结构元素，表明了科学论文的目标，方法或结果。我们介绍并描述了一个专家注释的测试集合，以评估培训的型号以执行此任务。我们的测试收集包括一个不同的50套英文查询文件，从计算语言学和机器学习场所绘制。我们仔细遵循TREC用于深度-K池（k = 100或250）使用的注释指南，结果数据收集包括具有高注释协议的分级相关性分数。在我们的数据集中评估的最先进模型显示出进一步的工作中的显着差距。可以在此处访问我们的数据集：https://github.com/iesl/csfcube

长文件摘要是自然语言处理领域的重要且艰巨的任务。良好的长文件摘要表现揭示了模型对人类语言的理解。目前，大多数研究侧重于如何修改变压器的注意机制，实现更高的胭脂分数。数据预处理和后处理的研究相对较少。在本文中，我们使用两个预处理方法和后处理方法，并分析了这些方法对各种长文件摘要模型的影响。

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。

In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information. In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task. MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks. Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we next propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available. Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation. Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model.

侧重于查询的摘要（QFS）需要生成使用一组相关文档的查询给出文本摘要。但是，在实践中，此类相关文件不易获得，但应首先从文档收集中检索。因此，我们展示了如何扩展此任务以使其更加逼真。因此，任务设置也类似于开放式域问题应答任务的设置，其中答案是顶部检索到的文档的摘要。要解决此扩展任务，我们将通过文本生成组合通过文本生成来产生给定输入查询的检索段落的摘要。我们展示了第一个对拟议任务的评估结果，并表明一些样本足以通过检索的通道进行微调的大型生成模型。

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.

自动摘要评估对于机器生成和人为生产的摘要都有用。自动评估给定文档的摘要文本启用，例如，摘要生成系统开发和检测不适当的摘要。摘要评估可以以多种模式进行：排名摘要生成系统；对特定文档的排名摘要；并在绝对规模上估算文档 - 苏格尔对的质量。带有注释的现有数据集用于摘要评估，通常基于新闻摘要数据集，例如CNN/DailyMail或XSUM。在这项工作中，我们描述了一个新的数据集，即播客摘要评估语料库，这是由TREC2020的人类专家评估的播客摘要集。与现有的摘要评估数据相比，该数据集具有两个独特的方面：（i）基于语音播客的长输入，文档； （ii）有机会在播客语料库中检测不适当的参考摘要。首先，我们检查了现有的评估方法，包括无模型和基于模型的方法，并为此长输入摘要评估数据集提供基准结果。其次，为了过滤参考参考文献配对以进行培训，我们采用摘要评估进行数据选择。这两个方面的实验结果为摘要评估和发电任务提供了有趣的见解。播客摘要评估数据可用。