Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news articles, short dialogues and guides. Different from these short texts, long documents such as academic articles and business reports usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, outperforming the strong pipeline models equipped with sophisticated machine translation systems. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.

跨语性摘要是用一种语言（例如英语）以不同语言（例如中文）生成一种语言（例如英语）的摘要。在全球化背景下，这项任务吸引了计算语言学界的越来越多的关注。然而，对于这项任务仍然缺乏全面的审查。因此，我们在该领域的数据集，方法和挑战上介绍了第一个系统的批判性审查。具体而言，我们分别根据不同的构造方法和解决方案范例仔细组织现有的数据集和方法。对于每种类型的数据集或方法，我们彻底介绍并总结了以前的努力，并将它们相互比较以提供更深入的分析。最后，我们还讨论了有希望的方向，并提供了我们的思想，以促进未来的研究。这项调查适用于跨语性摘要的初学者和专家，我们希望它将成为起点，也可以为对该领域感兴趣的研究人员和工程师提供新的想法。

Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS samples, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. Though many efforts have been devoted to CLS, none of them notice the phenomenon of translationese. In this paper, we first confirm that the different approaches to constructing CLS datasets will lead to different degrees of translationese. Then we design systematic experiments to investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in the real scene; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Furthermore, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.

体育游戏摘要旨在根据实时评论生成体育新闻。该任务吸引了广泛的研究关注，但由于缺乏相应的英语数据集，但仍未探索。因此，在本文中，我们发布了第一个英语体育游戏摘要数据集的目标。具体而言，目标有103个评论新对，评论和新闻的平均长度分别为2724.9和476.3个字。此外，为了支持半监督环境中的研究，目标还提供了2,160个未标记的评论文件。基于我们的目标，我们建立和评估了几个基线，包括提取性和抽象基线。实验结果表明，此任务的挑战仍然存在。我们希望我们的工作能够促进体育游戏总结的研究。该数据集已在https://github.com/krystalan/goal上发布。

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。

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。

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。

我们介绍了MTG，这是一套新的基准套件，用于培训和评估多语言文本生成。它是具有最大人类通知数据（400K）的第一次传播的多语言多路文本生成数据集。它包括五种语言（英语，德语，法语，西班牙语和中文）的四代任务（故事产生，问题生成，标题生成和文本摘要）。Multiway设置可以启用跨语言和任务的模型测试知识传输功能。使用MTG，我们从不同方面训练和分析了几种流行的多语言生成模型。我们的基准套件通过更多的人为宣传的并行数据促进了模型性能增强。它提供了各种一代方案的全面评估。代码和数据可在\ url {https://github.com/zide05/mtg}上获得。

对于多语言序列到序列预审预周序模型（多语言SEQ2SEQ PLM），例如姆巴特（Mbart），自制的预处理任务接受了多种单语言的培训，例如25种来自CommonCrawl的语言，而下游的跨语言任务通常在双语语言子集上进行，例如英语 - 德国人，存在数据差异，即领域的差异，以及跨语言学习客观差异，即在训练和填充阶段之间的任务差异。为了弥合上述跨语言域和任务差距，我们将使用额外的代码切换恢复任务扩展了香草预后管道。具体而言，第一阶段采用自我监督的代码转换还原任务作为借口任务，从而允许多语言SEQ2SEQ PLM获取一些域内对齐信息。在第二阶段，我们正常在下游数据上微调模型。 NLG评估（12个双语翻译任务，30个零射击任务和2项跨语言摘要任务）和NLU评估（7个跨语性自然语言推理任务）的实验表明，我们的模型超过了强大的基线MBART，具有标准的FINETUNNING，这表明了我们的模型策略，一致。分析表明，我们的方法可以缩小跨语性句子表示的欧几里得距离，并通过微不足道的计算成本改善模型概括。我们在：https：//github.com/zanchangtong/csr4mbart上发布代码。

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.

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.

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

Despite the current success of multilingual pre-training, most prior works focus on leveraging monolingual data or bilingual parallel data and overlooked the value of trilingual parallel data. This paper presents \textbf{Tri}angular Document-level \textbf{P}re-training (\textbf{TRIP}), which is the first in the field to extend the conventional monolingual and bilingual pre-training to a trilingual setting by (i) \textbf{Grafting} the same documents in two languages into one mixed document, and (ii) predicting the remaining one language as the reference translation. Our experiments on document-level MT and cross-lingual abstractive summarization show that TRIP brings by up to 3.65 d-BLEU points and 6.2 ROUGE-L points on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including multiple strong state-of-the-art (SOTA) scores. In-depth analysis indicates that TRIP improves document-level machine translation and captures better document contexts in at least three characteristics: (i) tense consistency, (ii) noun consistency and (iii) conjunction presence.

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.

Software engineers working with the same programming language (PL) may speak different natural languages (NLs) and vice versa, erecting huge barriers to communication and working efficiency. Recent studies have demonstrated the effectiveness of generative pre-training in computer programs, yet they are always English-centric. In this work, we step towards bridging the gap between multilingual NLs and multilingual PLs for large language models (LLMs). We release ERNIE-Code, a unified pre-trained language model for 116 NLs and 6 PLs. We employ two methods for universal cross-lingual pre-training: span-corruption language modeling that learns patterns from monolingual NL or PL; and pivot-based translation language modeling that relies on parallel data of many NLs and PLs. Extensive results show that ERNIE-Code outperforms previous multilingual LLMs for PL or NL across a wide range of end tasks of code intelligence, including multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation. We further show its advantage of zero-shot prompting on multilingual code summarization and text-to-text translation. We will make our code and pre-trained models publicly available.

Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users' interests. However, the current dataset for aspect or query-based summarization either focuses on specific domains, contains relatively small-scale instances, or includes only a few aspect types. Such limitations hinder further explorations in this direction. In this work, we take advantage of crowd-sourcing knowledge on Wikipedia.org and automatically create a high-quality, large-scale open-domain aspect-based summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. We provide benchmark results on OAsum and demonstrate its ability for diverse aspect-based summarization generation. To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets. Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model. Our dataset and pre-trained checkpoints are publicly available.

大型和超大语言模型的开发，例如GPT-3，T5，Switch Transformer，Ernie等，已经显着改善了文本生成的性能。该领域的重要研究方向之一是产生具有争论的文本。该问题的解决方案可以用于商务会议，政治辩论，对话系统，以准备学生论文。这些应用的主要领域之一是经济领域。俄罗斯语言的论证文本生成的关键问题是缺乏注释的论证语料库。在本文中，我们将论证的微观版，说服力论文和UKP句子语料库的翻译版本用于微调Rubert模型。此外，该模型用于通过论证注释经济新闻的语料库。然后使用带注释的语料库微调Rugpt-3模型，该模型生成参数文本。结果表明，与原始RUGPT-3模型相比，这种方法将论点生成的准确性提高了20个百分点（63.2％对42.5％）。

缺乏创造力的抽象方法在自动文本摘要中尤其是一个问题。模型产生的摘要主要是从源文章中提取的。该问题的主要原因之一是缺乏抽象性的数据集，尤其是对于中文而言。为了解决这个问题，我们用CLT中的参考摘要解释，中国长文本摘要数据集，正确的事实不一致的错误，并提出了第一个中国长文本摘要数据集，其中包含高度的clts+，其中包含超过更多的中文。 180k文章 - 苏格尔对，可在线购买。此外，我们引入了一个基于共发生词的固有度量，以评估我们构建的数据集。我们对CLTS+摘要中使用的提取策略进行了针对其他数据集的提取策略，以量化我们的新数据的抽象性和难度，并在CLTS+上训练多个基线，以验证IT的实用性以提高模型的创造力。

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.

通过自我监督的学习预先训练的大型语言模型在各种各样的任务上表现出令人印象深刻的零击功能。在这项工作中，我们介绍了Welm：一种针对中文的精心读取的预训练的语言模型，能够无缝执行不同类型的任务，以零或几次演示。 Welm通过“阅读”涵盖广泛主题的精选高质量语料库来接受10b参数的培训。我们表明，韦尔姆拥有有关各种领域和语言的广泛知识。在18个单语（中文）任务中，WELM可以大大优于现有的预训练模型，尺寸相似，并匹配高达25倍大的模型的性能。韦尔姆还表现出强大的多种语言和代码转换理解的能力，优于预先对30种语言进行预培训的现有多语言模型。此外，我们收集了人工编写的提示，并通过多次培训进行了大量的中文和微调韦尔姆的监督数据集。最终的模型可以实现对看不见的任务类型的强烈概括，并在零射门学习中优于无监督的韦尔姆。最后，我们证明韦尔姆具有解释和校准自己的决策的基本技能，这可能是未来研究的有希望的方向。我们的模型可以从https://welm.weixin.qq.com/docs/api/应用。