MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

Varab, Daniel; Schluter, Natalie

doi:10.18653/v1/2021.emnlp-main.797

Cited by 23 publications

(20 citation statements)

References 15 publications

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“…Conventional cross-lingual summarization methods mainly focus on incorporating bilingual information into the pipeline methods (Leuski et al, 2003;Ouyang et al, 2019;Orȃsan and Chiorean, 2008;Wan et al, 2010;Wan, 2011;Yao et al, 2015;Zhang et al, 2016b), i.e., translation and then summarization or summarization and then translation. Due to the difficulty of acquiring cross-lingual summarization dataset, some previous researches focus on constructing datasets (Ladhak et al, 2020;Scialom et al, 2020;Yela-Bello et al, 2021;Zhu et al, 2019;Hasan et al, 2021;Perez-Beltrachini and Lapata, 2021;Varab and Schluter, 2021), mixed-lingual pre-training (Xu et al, 2020), knowledge distillation (Nguyen and Tuan, 2021), contrastive learning (Wang et al, 2021) or zero-shot approaches (Ayana et al, 2018;Duan et al, 2019;Dou et al, 2020), i.e., using machine translation (MT) or monolingual summarization (MS) or both to train the CLS system. Among them, Zhu et al (2019) propose to use roundtrip translation strategy to obtain large-scale CLS datasets and then present two multi-task learning methods for CLS.…”

Section: Related Workmentioning

confidence: 99%

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

Liang¹,

Meng²,

Zhou³

et al. 2022

Preprint

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The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). Essentially, the CLS task is the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT&MS and CLS. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-toend model through an auxiliary MT or MS objective. However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize. To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder. The hierarchical model contains two kinds of latent variables at the local and global levels, respectively. At the local level, there are two latent variables, one for translation and the other for summarization. As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables. Experiments on two language directions (English⇔Chinese) verify the effectiveness and superiority of the proposed approach. In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the few-shot setting.

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Section: Related Workmentioning

confidence: 99%

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

Liang¹,

Meng²,

Zhou³

et al. 2022

Preprint

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“…For instance, Wikipedia has been used as a resource to derive multilingual benchmarks (Botha et al, 2020;Liu et al, 2019a;Pan et al, 2017;Rahimi et al, 2019), and several multilingual summarisation datasets have been created by extracting article-summary pairs from online newspapers or how-to guides (e.g. Hasan et al, 2021;Ladhak et al, 2020;Nguyen and Daumé III, 2019;Scialom et al, 2020;Varab and Schluter, 2021). Various linguistic resources have also been exploited: for instance, the Universal Dependencies treebank (Nivre et al, 2020) has been used to evaluate cross-lingual part-of-speech tagging, and multilingual WordNet and Wiktionary have been used to build XL-WiC (Raganato et al, 2020), an extension of WiC (Pilehvar and Camacho-Collados, 2019) that reformulates word sense disambiguation in 12 languages as a binary classification task.…”

Section: Generalisation Across Languagesmentioning

confidence: 99%

State-of-the-art generalisation research in NLP: A taxonomy and review

Hupkes¹,

Giulianelli²,

Dankers³

et al. 2022

Preprint

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The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what 'good generalisation' entails and how it should be evaluated is not well understood, nor are there any common standards to evaluate it. In this paper, we aim to lay the groundwork to improve both of these issues. We present a taxonomy for characterising and understanding generalisation research in NLP, we use that taxonomy to present a comprehensive map of published generalisation studies, and we make recommendations for which areas might deserve attention in the future. Our taxonomy is based on an extensive literature review of generalisation research, and contains five axes along which studies can differ: their main motivation, the type of generalisation they aim to solve, the type of data shift they consider, the source by which this data shift is obtained, and the locus of the shift within the modelling pipeline. We use our taxonomy to classify over 400 previous papers that test generalisation, for a total of more than 600 individual experiments. Considering the results of this review, we present an in-depth analysis of the current state of generalisation research in NLP, and make recommendations for the future. Along with this paper, we release a webpage where the results of our review can be dynamically explored, and which we intend to update as new NLP generalisation studies are published. With this work, we aim to make steps towards making state-of-the-art generalisation testing the new status quo in NLP.

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“…Even though it is a large highquality resource of parallel data for cross-lingual summarization, this corpus is built from how-to guides: our dataset focuses instead on scholarly documents. Besides cross-lingual corpora, there are also large-scale multilingual summarization datasets for the news domain [48,52]. The work we present here differs in that we focus on extreme summarization for the scholarly domain and we look specifically at the problem of cross-lingual summarization in which source and target language differ.…”

Section: Related Workmentioning

confidence: 99%

“…Just like in virtually all areas of NLP research, most successful approaches to summarization rely on neural techniques using supervision from labeled data. This includes neural models to summarize documents in general domains such as news articles [33,49], including cross-and multi-lingual models and datasets [48,52], as well as specialized ones e.g., the biomedical domain [39].…”

Section: Introductionmentioning

confidence: 99%

X-Scitldr

Takeshita

Green

Friedrich

et al. 2022

Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

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The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Consequently, recent work on applying text mining technologies for scholarly publications has investigated the application of automatic text summarization technologies, including extreme summarization, for this domain. However, previous work has concentrated only on monolingual settings, primarily in English. In this paper, we fill this research gap and present an abstractive cross-lingual summarization dataset for four different languages in the scholarly domain, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage 'summarize and translate' approach and a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero-and few-shot scenarios. CCS Concepts• Computing methodologies → Natural language processing; Natural language generation; Language resources.

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MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

Cited by 23 publications

References 15 publications

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

State-of-the-art generalisation research in NLP: A taxonomy and review

X-Scitldr

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