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。
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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
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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。
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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
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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.
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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。
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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。
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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。
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Modern multi-document summarization (MDS) methods are based on transformer architectures. They generate state of the art summaries, but lack explainability. We focus on graph-based transformer models for MDS as they gained recent popularity. We aim to improve the explainability of the graph-based MDS by analyzing their attention weights. In a graph-based MDS such as GraphSum, vertices represent the textual units, while the edges form some similarity graph over the units. We compare GraphSum's performance utilizing different textual units, i. e., sentences versus paragraphs, on two news benchmark datasets, namely WikiSum and MultiNews. Our experiments show that paragraph-level representations provide the best summarization performance. Thus, we subsequently focus oAnalysisn analyzing the paragraph-level attention weights of GraphSum's multi-heads and decoding layers in order to improve the explainability of a transformer-based MDS model. As a reference metric, we calculate the ROUGE scores between the input paragraphs and each sentence in the generated summary, which indicate source origin information via text similarity. We observe a high correlation between the attention weights and this reference metric, especially on the the later decoding layers of the transformer architecture. Finally, we investigate if the generated summaries follow a pattern of positional bias by extracting which paragraph provided the most information for each generated summary. Our results show that there is a high correlation between the position in the summary and the source origin.
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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。
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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.
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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。
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本文介绍了Z-Code ++,这是一种针对抽象文本摘要优化的新的预训练的语言模型。该模型使用三种技术扩展了艺术编码器模型的状态。首先,我们使用两阶段的预训练过程来改善模型在低资源摘要任务上的性能。该模型首先是使用文本语料库进行语言理解的预先培训的,然后在汇总语料库中不断预先培训,以进行基础文本生成。其次,我们用分离的注意力层代替编码器中的自我发项层,其中每个单词都使用两个向量分别代表其内容和位置。第三,我们使用融合编码器,这是一种以层次方式编码长序列的简单而有效的方法。 Z-Code ++在13个文本摘要任务中的9个跨5种语言中创建了新的艺术状态。我们的模型的参数有效,因为它的表现优于XSUM上600倍较大的Palm-540b,并且在Samsum上的易经的200倍GPT3-175B较大。在零射击和少量设置中,我们的模型大大优于竞争模型。
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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%)。
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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.
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寻求健康信息的寻求使网络与消费者的健康相关问题淹没了。通常,消费者使用过度描述性和外围信息来表达其医疗状况或其他医疗保健需求,从而有助于自然语言理解的挑战。解决这一挑战的一种方法是总结问题并提取原始问题的关键信息。为了解决此问题,我们介绍了一个新的数据集CHQ-SUMM,其中包含1507个域 - 专家注释的消费者健康问题和相应的摘要。该数据集源自社区提问论坛,因此为了解社交媒体上与消费者健康相关的帖子提供了宝贵的资源。我们在多个最先进的摘要模型上基准测试数据集,以显示数据集的有效性。
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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.
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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.
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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。
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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
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