dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainty and sensitivity analyses As shown in the previous sections, criticality assessments are unavoidably affected by many uncertainties and methodological choices, e. g. regarding the acceptable quality of the data, the selection of materials, the choice of data sources, and the choice of aggregation methods. The robustness of the criticality results is strongly dependent on the transparent communication of (the effect of) uncertainties and assumptions. For example,  Helbig et al. (2016a)  evaluate the influence of aggregation methods on the final outcomes via a sensitivity analysis and conduct uncertainty analyses. Graedel et al. (2015)  present the relative criticality of each material in the form of a "criticality cloud" that accounts for uncertainty ranges. As very few methods report uncertainty ranges of the criticality results, it is not known whether the assignment of materials as "critical" is generally robust, especially in studies that apply criticality threshold values.
Acknowledgements The  IRTC  project has received funding from the  EIT RawMaterials , supported by the  Institute of Innovation and Technology (EIT) , a body of the  European Union , under the  Horizon 2020 , the  EU Framework Programme for Research and Innovation. The authors would like to thank  Vanessa Bach ,  Ton Bastein ,  Britta Bookhagen ,  Hiroki Hatayama ,  Alan Hurd ,  Risto Krebs ,  Gavin Mudd ,  Nedal Nassar ,  Tanya Tsui , and  Alicia Valero  for contributing to the  Criticality Library (SI-A)  and the  Goal and Scope table (SI-B). Furthermore, we thank  René Kleijn , Ester van der Voet, and  Steven Young  for their contributions to the initial scoping of the paper,  Ton Bastein ,  Julian Hilton ,  Dominique Guyonnet , and the two anonymous reviewers for their comments, and  Tom Graedel  for his contributions to the discussion.
Genetic diversity is estimated to be declining faster than species diversity under escalating threats, but its spatial distribution remains poorly documented at the global scale. Theory predicts that similar processes should foster congruent spatial patterns of genetic and species diversity, but empirical studies are scarce. Using a mined database of 50,588 georeferenced mitochondrial DNA barcode sequences (COI) for 3,815 marine and 1,611 freshwater fish species respectively, we examined the correlation between genetic diversity and species diversity and their global distributions in relation to climate and geography. Genetic diversity showed a clear spatial organisation, but a weak association with species diversity for both marine and freshwater species. We found a predominantly positive relationship between genetic diversity and sea surface temperature for marine species.
Whether these patterns hold at the intraspecific level, i. e. genetic diversity, has not yet been investigated. Here, from a macro-genetic perspective, we study the global distribution of genetic diversity in ray-finned fishes using data for 1611 freshwater and 3815 marine species. Genetic diversity patterns are produced by assembling 50,588 georeferenced mitochondrial sequences in the Barcode of Life Database (BOLD). We then estimate nucleotide diversity for each species in each grid cell at a spatial resolution of 200 km. This nucleotide diversity is averaged across the species in each cell. We first investigate the correlation between genetic and species diversity separately for marine and freshwater fishes. Next, we explore the global environmental and geographic determinants of the mean nucleotide diversity across species per cell, hereafter called genetic diversity.
It is   3  and 5 for details on models). Coefficients and confidence intervals for the factors of the models for marine (a) and freshwater fishes (c). Confidence intervals were estimated from the standard error of each coefficient at a level of 5% and were obtained with the command confint in the R package lm. Autocor is a spatial autocovariate that takes into account spatial autocorrelation in both our predicted and predictive variables. Relative variance of genetic diversity explained by the various factors was estimated and represented as partial plots with the package hier. part in marine (b) and in freshwater fishes (e). noteworthy that at a given latitude, habitat difference (freshwater vs. marine) explains 67% of partial variation in genetic diversity (Supplementary Table  3 ). Every measure of diversity has a dependence on the spatial scale.
These differences might be explained by disparities in temporal and spatial scales or in responses to environmental changes at which parallel ecological and evolutionary processes operate (mutation vs. speciation; genetic vs. ecological drift; gene flow vs. dispersal; selection vs. environmental filter)  54. Although spatially extensive, our study has some limitations associated with the data mining and the analyses performed. The samples used in Fig. 1  represent only 26% of marine and 11% of freshwater species worldwide, but cover globally 100% of fish orders and 70% of families (Supplementary Fig. 5 ; Supplementary Table  9 ). When one-third of the cells with at least two species and more than four sequences for marine species and five sequences for freshwater species were filtered out, the taxonomic coverage of our full dataset only decreased to 95% for orders and 61% for families.
With this stringent filter based on minimum absolute number of sampled species and consistently high global taxonomic coverage, the correlations between genetic and species diversity increase and the significance of the main parameters remains nearly significant (Supplementary Table  7 ). In all other cases, the global taxonomic coverage (of our full dataset) is largely maintained (98% of orders and 69% of families covered). The dataset has some major gaps across the globe, which might influence the estimation of associations between genetic diversity and environmental factors (Supplementary Fig. 6 ). For example, the African realm has been largely under-sampled compared to other realms (Supplementary Fig. 6 ). Sampling efforts should be expanded to improve the global coverage of biodiversity information at the intraspecific level  18, 31.
Mapping fish genetic diversity. Mean fish genetic diversity per cell was estimated across all species and mapped on a grid cell covering the study area for marine (Fig. 1a ) and freshwater species (Fig. 1c ). We created the worldwide grid at 200 km resolution using an equal area Behrmann projection. We selected the 1299 cells containing sequence collection by making an intersection between the grid and the coordinates of the sequence using the gIntersects function from the rgeos package in R. Multiple sequence alignments of the 58,565 COI sequences of the 5912 species using MUSCLE3 were performed to estimate genetic diversity per cell  65. The alignments were checked manually using the software ugene  66. In addition, only pairwise alignments with overlap >50% were kept to calculate genetic diversity.
We estimated the genetic diversity (GD) in each cell of the grid as the mean of all species nucleotide diversities averaged across species. Mapping fish species diversity. Marine fish species data were obtained from the Ocean Biogeographic Information System (OBIS, http://www. iobis. org). We inventoried 16,238,200 occurrence records from 34,883 entries. We cleaned the data by identifying synonyms, misspellings and rare species (only one occurrence) and restricted them to species present in the marine environment according to FishBase  67. We reconstructed distribution maps for each species, defined as the convex polygon surrounding the area where each species was observed. The resulting polygon was divided into four parts across the world to integrate possible discontinuity between the two hemispheres and the Atlantic and Pacific Oceans.
For example, antitropical species are distributed in northern and southern hemisphere, but show range discontinuity near the tropics  69  and a polygon division can account for this singularity. We refined each species distribution map by removing areas where maximum depths fell outside the minimum or maximum known depth range of the species  70. As the OBIS database does not properly represent tropical fish assemblages, we merged this database with the Gaspar database at 1°resolution that encompass 6316 coral reef species  4. We obtained a world database containing most marine fish species that we aggregated on a 1°r esolution grid covering all oceans, as this resolution is useful for other projects. A freshwater dataset was obtained from the occurrence data provided by Tedesco et al. 71.
We first compiled the polygon of each available species from the occurrence table, and then aggregated this information into a presence-absence matrix. We obtained a world database containing freshwater fish species on a 1°resolution grid covering all terrestrial parts of the Earth. Considering the scale and resolution of the current study (200 km), our polygons represent the distribution of species with sufficient accuracy compared to those commonly used in macroecological studies  72, 73. For marine and freshwater species, genetic diversity per cell was aggregated by latitude bands of 10°. We then plotted the genetic diversity per band of latitude using R. The confidence interval for genetic diversity by latitude band (standard deviation of the genetic diversity per cell divided by the square root of the number of cells) was reported in the plot representing the variability of genetic diversity at latitudinal bands amongst cells (Fig.
Regions were defined as Atlantic vs Indo-pacific for marine species and Africa, Antarctica, Europe, North America, Oceania and South America for freshwater species. We also included the effect of sampling in all models (number of sequences and number of species in each cell of the grid). Before any calculations were made, we checked for factor collinearity in the model using a variance inflation factor (VIF) procedure. Highly collinear variables with a VIF >5 were removed from the model. Both the environmental factors and the diversity metrics inevitably show some spatial autocorrelation. We investigated the spatial autocorrelation in genetic diversity with a Moran spatial autocorelogramme using the R function pgi. cor (package pgirmess). We built an autocovariate variable for the fish genetic diversity metrics (function autocov_dist in spdep R package) to account for spatial dependency and estimate how any cell reflects the values of the neighbouring cells  75.
Acknowledgements We thank  Michel Kulbicki  for his helpful input. This work benefited from the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR  " Investissements d'avenir" programme  ( ANR-10-LABX-04-01 ). P. E. is funded by 2015 -2016 BiodivERsA COFUND call for research proposals, with the national funders  ANR (France). We would also like to thank the scientists who collected the data and generated the sequences used in the meta-analysis. L. P. was supported by the  SNF  project  REEFISH  no. 310030E-164294.
Data availability The datasets generated during and/or analysed during the current study are available in the worldmap_fish_genetic_diversity repository, https://gitlab. mbb. univ-montp2. fr/ reservebenefit/worldmap_fish_genetic_diversity. The source data underlying Fig. 1b, d , 2a, c, 3a, c and Supplementary Figs. 1 to 7 are provided as a Source Data file.
We present MLSUM, the first large-scale Mul-tiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1. 5M+ article/summary pairs in five different languages -namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
Introduction The document summarization task requires several complex language abilities: understanding a long document, discriminating what is relevant, and writing a short synthesis. Over the last few years, advances in deep learning applied to NLP have contributed to the rising popularity of this task among the research community  (See et al. , 2017; Kryściński et al. , 2018; Scialom et al. , 2019). As with other NLP tasks, the great majority of available datasets for summarization are in English, and thus most research efforts focus on the English language. The lack of multilingual data is partially countered by the application of transfer learning techniques enabled by the availability of pre-trained multilingual language models. This approach has recently established itself as the de-facto paradigm in NLP  (Guzmán et al.
, 2019). Under this paradigm, for encoder/decoder tasks, a language model can first be pre-trained on a large corpus of texts in multiple languages. Then, the model is fine-tuned in one or more pivot languages for which the task-specific data are available. At inference, it can still be applied to the different languages seen during the pre-training. Because of the dominance of English for large scale corpora, English naturally established itself as a pivot for other languages. The availability of multilingual pre-trained models, such as BERT multilingual (M-BERT), allows to build models for target languages different from training data. However, previous works reported a significant performance gap between English and the target language, e. g. for classification  (Conneau et al. , 2018)  and Question Answering  (Lewis et al.
, 2019)  tasks. A similar approach has been recently proposed for summarization  (Chi et al. , 2019)  obtaining, again, a lower performance than for English. For specific NLP tasks, recent research efforts have produced evaluation datasets in several target languages, allowing to evaluate the progress of the field in zero-shot scenarios. Nonetheless, those approaches are still bound to using training data in a pivot language for which a large amount of annotated data is available, usually English. This prevents investigating, for instance, whether a given model is as fitted for a specific language as for any other. Answers to such research questions represent valuable information to improve model performance for low-resource languages. In this work, we aim to fill this gap for the automatic summarization task by proposing a largescale MultiLingual SUMmarization (MLSUM) dataset.
The dataset is built from online news outlets, and contains over 1. 5M article-summary pairs in 5 languages: French, German, Spanish, Russian, and Turkish, which complement an already established summarization dataset in English. The contributions of this paper can be summarized as follows: 1. We release the first large-scale multilingual summarization dataset; 2. We provide strong baselines from multilingual abstractive text generation models; 3. We report a comparative cross-lingual analysis of the results obtained by different approaches. 2 Related Work.
Multilingual Text Summarization Over the last two decades, several research works have focused on multilingual text summarization. Radev et al. (2002)  developed MEAD, a multidocument summarizer that works for both English and Chinese. Litvak et al. (2010)  proposed to improve multilingual summarization using a genetic algorithm. A community-driven initiative, MultiLing  (Giannakopoulos et al. , 2015) , benchmarked summarization systems on multilingual data. While the MultiLing benchmark covers 40 languages, it provides relatively few examples (10k in the 2019 release). Most proposed approaches, so far, have used an extractive approach given the lack of a multilingual corpus to train abstractive models  (Duan et al. , 2019). More recently, with the rapid progress in automatic translation and text generation, abstractive methods for multilingual summarization have been developed.
Ouyang et al. (2019)  proposed to learn summarization models for three low-resource languages (Somali, Swahili, and Tagalog), by using an automated translation of the New York Times dataset. Although this showed only slight improvements over a baseline which considers translated outputs of an English summarizer, results remain still far from human performance. Summarization models from translated data usually under-perform, as translation biases add to the difficulty of summarization. Following the recent trend of using multi-lingual pre-trained models for NLP tasks, such as Multilingual BERT (M-BERT)  (Pires et al. , 2019)   1  or XLM  (Lample and Conneau, 2019) ,  Chi et al. (2019)  proposed to fine-tune the models for summarization on English training data. The assumption is that the summarization skills learned from English data can transfer to other languages on which the model has been pre-trained.
Existing Multilingual Datasets The research community has produced several multilingual datasets for tasks other than summarization. We report two recent efforts below, noting that both i) rely on human translations, and ii) only provide evaluation data. The Cross-Lingual NLI Corpus The SNLI corpus  (Bowman et al. , 2015)  is a large scale dataset for natural language inference (NLI). It is composed of a collection of 570k human-written English sentence pairs, associated with their label, entailment, contradiction, or neutral. The Multi-Genre Natural Language Inference (MultiNLI) corpus is an extension of SNLI, comparable in size, but including a more diverse range of text. Conneau et al. (2018)  introduced the Cross-Lingual NLI Corpus (XNLI) to evaluate transfer learning from English to other languages: based on MultiNLI, a collection of 5,000 test and 2,500 dev pairs were translated by humans in 15 languages.
MLQA Given a paragraph and a question, the Question Answering (QA) task consists in providing the correct answer. Large scale datasets such as  (Rajpurkar et al. , 2016; Choi et al. , 2018; Trischler et al. , 2016)  have driven fast progress. 2 However, these datasets are only in English. To assess how well models perform on other languages,  Lewis et al. (2019)  recently proposed MLQA, an evaluation dataset for cross-lingual extractive QA composed of 5K QA instances in 7 languages. XTREME The Cross-lingual TRansfer Evaluation of Multilingual Encoders benchmark covers 40 languages over 9 tasks. The summarization task is not included in the benchmark. XGLUE In order to train and evaluate their performance across a diverse set of cross-lingual tasks,  Liang et al. (2020)  recently released XGLUE, covering both Natural Language Understanding and Generation scenarios.
Existing Summarization datasets We describe here the main available corpora for text summarization. Document Understanding Conference Several small and high-quality summarization datasets in English  (Harman and Over, 2004; Dang, 2006)  have been produced in the context of the Document Understanding Conference (DUC). 3 They are built by associating newswire articles with corresponding human summaries. A distinctive feature of the DUC datasets is the availability of multiple reference summaries: this is a valuable characteristic since, as found by  Rankel et al. (2013) , the correlation between qualitative and automatic metrics, such as ROUGE  (Lin, 2004) , decreases significantly when only a single reference is given. However, due to the small number of training data available, DUC datasets are often used in a domain adaptation setup for models first trained on larger datasets such as Gigaword, CNN/DM  (Nallapati et al.
Although  (Grusky et al. , 2018)  found indications of bias towards extractive approaches, several research efforts have used this dataset for summarization  (Hong and Nenkova, 2014; Durrett et al. , 2016; Paulus et al. , 2017). CNN / Daily Mail One of the most commonly used dataset for summarization  (Nallapati et al. , 2016; See et al. , 2017; Paulus et al. , 2017; Dong et al. , 2019) , although originally built for Question Answering tasks  (Hermann et al. , 2015a). It consists of English articles from the CNN and The 3 http://duc. nist. gov/ Daily Mail associated with bullet point highlights from the article. When used for summarization, the bullet points are typically concatenated into a single summary. NEWSROOM Composed of 1. 3M articles  (Grusky et al. , 2018) , and featuring high diversity in terms of publishers, the summaries associated with English news articles were extracted from the Web pages metadata: they were originally written to be used in search engines and social media.
MLSUM As described above, the vast majority of summarization datasets are in English. For Arabic, there exist the Essex Arabic Summaries Corpus (EASC)  (El-Haj et al. , 2010)  and  KALIMAT (El-Haj and Koulali, 2013) ; those comprise circa 1k and 20k samples, respectively. Pontes et al. (  2018 ) proposed a corpus of few hundred samples for Spanish, Portuguese and French summaries. To our knowledge, the only large-scale non-English summarization dataset is the Chinese LCSTS  (Hu et al. , 2015). With the increasing interest for cross-lingual models, the NLP community have recently released multilingual evaluation datasets, targeting classification (XNLI) and QA  (Lewis et al. , 2019)  tasks, as described in 2. 2, though still no large-scale dataset is avaulable for document summarization. To fill this gap we introduce MLSUM, the first large scale multilingual summarization corpus.
Our corpus provides more than 1. 5 millions articles in French (FR), German (DE), Spanish (ES), Turkish (TR), and Russian (RU). Being similarly built from news articles, and providing a similar amount of training samples per language (except for Russian), as the previously mentioned CNN/Daily Mail, it can effectively serve as a multilingual extension of the CNN/Daily Mail dataset. In the following, we first describe the methodology used to build the corpus. We then report the corpus statistics and finally interpret the performances of baselines and state-of-the-art models.
Collecting the Corpus The CNN/Daily Mail (CNN/DM) dataset (see Section 2. 3) is arguably the most used large-scale dataset for summarization. Following the same methodology, we consider news articles as the text input, and their paired highlights/description as the summary. For each language, we selected an online newspaper which met the following requirements: 1. Being a generalist newspaper: ensuring that a broad range of topics is represented for each language allows to minimize the risk of training topic-specific models, a fact which would hinder comparative cross-lingual analyses of the models. 2. Having a large number of articles in their public online archive. 3. Providing human written highlights/summaries for the articles that can be extracted from the HTML code of the web page. After a careful preliminary exploration, we selected the online version of the following newspapers: • Le Monde 4  (French) • Süddeutsche Zeitung 5  (German) • El Pais 6  (Spanish) • Moskovskij Komsomolets 7  (Russian) • Internet Haber 8  (Turkish) For each outlet, we crawled archived articles from 2010 to 2019.
We applied one simple filter: all the articles shorter than 50 words or summaries shorter than 10 words are discarded, so as to avoid articles containing mostly audiovisual content. Each article was archived on the Wayback Machine, 9  allowing interested research to re-build or extend MLSUM. We distribute the dataset as a list of immutable snapshot URLs of the articles, along with the accompanying corpus-construction code, 10  allowing to replicate the parsing and preprocessing procedures we employed. This is due to legal reasons: the content of the articles is copyrighted and redistribution might be seen as infringing of publishing rights. Nonetheless, we make available, upon request, an exact copy of the dataset used in this work. A similar approach has been adopted for several dataset releases in the recent past, such as Question Answering Corpus  (Hermann et al.