dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter includes 500 news annotated by crowdsourcing. Both datasets are very small and contain newswire articles from various online sources labelled by non-expert annotators, in this case readers, which is not without issues. Thus, unlike author annotations, those produced by readers exhibit significantly lower missing keyphrases, that is, gold keyphrases that do not occur in the content of the document. In the DUC-2001 dataset for example, more than 96% of the gold keyphrases actually appear in the documents. This confirms previous observations that readers tend to assign keyphrases in an extractive fashion  (Wang et al. , 2015) , which makes these datasets less suitable for the task at hand (keyphrase generation) but rather relevant for a purely extractive task (keyphrase extraction).
Yet, author-assigned keyphrases commonly found in scientific paper datasets are not perfect either, as they are less constrained  (Sood et al. , 2007)  and include seldom-used variants or misspellings that negatively impact performance. One can see there is an apparent lack of sizeable expert-annotated data that enables the development of neural keyphrase generation models in a domain other than scholarly texts. Here, we fill this gap and propose a large-scale dataset that includes news texts paired with manually curated gold standard annotations.
Building the KPTimes dataset To create the KPTimes dataset, we collected over half a million newswire articles by crawling selected online news websites. We applied heuristics to identify the content (title, headline and body) of each article and regarded the keyphrases provided in the HTML metadata as the gold standard. A cherry-picked sample document is showcased in Figure  1 , it allows to show present and absent keyphrases, as well as keyphrase variants (in this example News media and journalism). We use the New York Times 1  as our primary source of data, since the content tagging policy that it applies is rigorous and well- documented 2. The news articles are annotated in a semi-automatic way, first the editors revise a set of tags proposed by an algorithm. They then provide additional tags which will be used by a taxonomy team to improve the algorithm.
We first retrieved the URLs of the free-to-read articles from 2006 to 2017 3  , and collected the corresponding archived HTML pages using the Internet Archive 4. Doing so allows the distribution of our dataset using a thin, URL-only list. We then extracted the HTML body content using beautifulsoup 5  and devised heuristics to extract the main content and title of each article while excluding extraneous HTML markup and inline ads. Gold standard keyphrases are obtained from the metadata (field types news keywords and keywords 6  ) available in the HTML page of each article. Surface form variants of gold keyphrases (e. g. "AIDS; HIV", "Driverless Cars; Self-Driving Cars" or "Fatalities; Casualties"), which are sometimes present in the metadata, are kept to be used for evaluation purposes. We further cleansed and filtered the dataset by removing duplicates, articles without content and those with too few (less than 2) or too many (more than 10) keyphrases.
This process resulted in a set of 279 923 article-keyphrase pairs. We randomly divided this dataset into training (92. 8%), development (3. 6%) and test (3. 6%) splits. Restricting ourselves to one source of data ensures the uniformity and consistency of annotation that is missing in the other datasets, but it may also make the trained model source-dependent and harm generalization. To monitor the model's ability to generalize, we gather a secondary source of data. We collected HTML pages from the Japan Times 7  and processed them the same way as described above. 10K more news articles were gathered as the JPTimes dataset. Although in this study we concentrate only on the textual content of the news articles, it is worth noting that the HTML pages also provide additional information that can be helpful in generating keyphrases such as text style properties (e.
Data analysis We explored the KPTimes dataset to better understand how it stands out from the existing ones. First, we looked at how editors tag news articles. Figure  2  illustrates the difference between the annotation behaviour of readers, authors and editors through the number of times that each unique keyphrase is used in the gold standard. We see that non-expert annotators use a larger, less controlled indexing vocabulary, in part because they lack the higher level of domain expertise that editors have. For example, we observe that frequent keyphrases in KPTimes are close to topic descriptors (e. g. "Baseball", "Politics and Government") while those appearing only once are very precise (e. g. "Marley's Cafe", "Catherine E. Connelly"). Annotations in KPTimes are arguably more uniform and consistent, through the use of tag suggestions, which, as we will soon discuss in §5.
3, makes it easier for supervised approaches to learn a good model. Next, we further looked at the characteristics of the gold keyphrases in KPTimes. Table  1  shows that the number of gold keyphrases per document is similar to the one observed for KP20k while the number of missing keyphrases is higher. This indicates that editors are more likely to generalize and assign keyphrases that do not occur in the document (≈ 55%). It is therefore this ability to generalize that models should mimic in order to perform well on KPTimes. We also note that keyphrases are on average shorter in news datasets (1. 5 words) than those in scientific paper datasets (2. 4 words). This may be due to the abundant use of longer, more specific phrases in scholarly documents  (Jin et al. , 2013). Variants of keyphrases recovered from the metadata occur in 8% of the documents and represent 810 sets of variants in the KPTimes test split.
State-of-the-art, supervised: CopyRNN The generative neural model we include in this study is CopyRNN  (Meng et al. , 2017) , an encoder-decoder model that incorporates a copying mechanism  (Gu et al. , 2016)  in order to be able to generate phrases that rarely occur. When properly trained, this model was shown to be very effective in extracting keyphrases from scientific abstracts. CopyRNN has been further extended by  (Chen et al. , 2018)  to include correlation constraints among keyphrases which we do not include here as it yields comparable results. Two models were trained to bring evidence on the necessity to have datasets from multiple domains. CopySci was trained using scientific abstracts (KP20k) and CopyNews using newspaper articles (KPTimes), the two models use the same architecture.
Results Model performances for each dataset are reported in Table  2. Extractive baselines show the best results for KPCrowd and DUC-2001 which is not surprising given that these datasets exhibit the lowest ratio of absent keyphrases. Neural-based models obtain the greatest performance, but only for the dataset on which they were trained. We therefore see that these models do not generalize well across domains, confirming previous preliminary findings  (Meng et al. , 2017)  and exacerbating the need for further research on this topic. Interestingly, CopyNews outperforms the other models on JPTimes and achieves very low scores for KPCrowd and DUC-2001, although all these datasets are from the same domain. This emphasizes the differences that exist between the readerand editor-assigned gold standard.
The score difference may be explained by the ratio of absent keyphrases that differs greatly between the readerannotated datasets and JPTimes (see Table  1 ), and thus question the use of these rather extractive datasets for evaluating keyphrase generation. Finally, we note that the performance of Copy-News on KPTimes is significantly higher than that of CopySci on KP20k, proving that a more uniform and consistent annotation makes it easier to learn a good model.
Conclusion In this paper we presented KPTimes, a large-scale dataset of newswire articles to train and test deep learning models for keyphrase generation. The dataset and the code are available at https: //github. com/ygorg/KPTimes. Large datasets have driven rapid improvement in other natural language generation tasks, such as machine translation or summarization. We hope that KPTimes will play this role and help the community in devising more robust and generalizable neural keyphrase generation models.
Dataset Ann. #Train #Dev #Test #words #kp len kp %abs Statistics of available datasets for keyphrase generation. Gold annotation is performed by authors (A), readers (R) or editors (E). The number of documents in the training (#Train), validation (dev) and testing (#Test) splits are shown. The average number of keyphrases (#kp) and words (#words) per document, the average length of keyphrases (len kp) and the ratio of keyphrases in the reference that do not appear in the document (%abs) are computed on the test set.
These systems are difficult to integrate with existing ERPs and business intelligence systems typically installed in healthcare buildings, and therefore the data they collect (from their sensors), and the tasks they execute are not registered in the corporate records and data management system. Cybersecurity threat is another deterrent factor, especially in cases where humans are involved. The solution to the limitations above, and the overall goal of the ENDORSE concept is to design and develop integrated robotic logis systems that enable efficient, cost-effective and safe indoor logistic services for hospital spaces. The specific objectives of ENDORSE, that should be successfully implemented, are described next: 1) To develop a cost-effective logistics robotics system for indoor healthcare environments.
A key point for the achievement of this goal is the minimization of installation time and the cost of the system. 2) To push forward the frontier of Human-Robot Interaction, especially for the cases where the robot should safely navigate in crowded spaces. It is human-robot interaction that should help to resolve deadlocks that involve humans. 3) To develop robust localization methods for infrastructureless indoor fleet localization and supervision, which will result in installation time minimization. 4) To introduce innovative cloud services that bring together solutions from healthcare, robotics, and logistics and will offer new business models for indoor logistics and high data security standards. 5) To develop an integrated e-diagnostic mobile station, which will endow modularity to the ENDORSE system, and will serve as an e-diagnostic mobile station.
CONCEPT DESCRIPTION The ENDORSE project will realize the objectives above by relying on four main innovation pillars: 1. Infrastructureless multi-robot navigation: Novel internet-based indoors localization technologies will be developed. These will enable multi-robot navigation requiring minimum or no installation of infrastructure on building walls, ceilings and floors (i. e. minimum sensors and communications buses). A typical sensor installation costs over 10,000 euros and requires several days for installation and integration. Hence, this approach will considerably reduce the cost and the time needed for infrastructure installation of indoor The infrastructureless multi-robot navigation will be implemented by a software architecture that will combine ROS packages implementing the RB1-base navigations and path planning and Java and JavaScript modules for the GUI and the Fleet management system implementation.
That will require research and development in both non-invasive sensors/devices (ECG, blood pressure, oximeter, temperature) and dedicated software applications. To this end, modular hardware interfaces (mechanical, electrical and data interfaces) mounted on the chassis of the robot (commercially available RB1-base by Robotnik as shown in Figure  1 ), so that different hardware functional modules can be easily swapped. That would be the first time that a medical diagnostic functionality is integrated on a mobile robot. The integrated robot will serve as an e-diagnostic mobile station, which will be able to produce a diagnostic exam report that will be securely forwarded to update and complement the patient's cloud-based Electronic Health Record (EHR). The software of the e-diagnostic mobile station will run on the robot.
Cloud servers will be able to connect with different individual applications and enterprise legacy systems. The cloud service proposed will contain a repository of algorithms, which will be fed by the data gathered through the cloud. The algorithms in the cloud will allow supporting the management of fleet, including the assignment of tasks to the robots, the queuing functionalities, the generation of dynamic routing and traffic control of the robots, and the supervision the fleet's task execution. Developing a cloud-oriented software platform will facilitate the development of APIs for integrating ENDORSE software with corporate ERP solutions and health record systems that are often provided as a service in a Cloud environment. Most importantly the ENDORSE applications will comply with the high safety standards for data sharing and data storage which are offered by the Cloud infrastructure.
TECHNOLOGICAL SOLUTIONS Cloud-based services In the project, a creation of an API framework will be accomplished, which will integrate logistic robotics into the corporate IT environment, and enable end-to-end automation (automatic traceability of the transferred goods, update of the ERP databases when transactions are carried out, etc. ). Moreover, communication and security standards will be investigated in order to securely transfer data from the robot to the patient's cloud-based EHR. To realize this task, ROS-based software integration will be pursued, where all system modules will be represented by ROS packages and ROS nodes. Non-ROS modules (i. e. Java or JavaScript applications) will have APIs that create objects that can be translated by ROS bridge package into ROS standard objects (topics, services, actions, etc.
). Due to its integrative nature, this task will be implemented iteratively and will be tightly interleaved with safety human-robot interaction protocols. An initial integrated software prototype will be developed given the prioritized system requirements and the output of the implementation. Then this initial prototype will be integrated with the Cloud-based services. The evaluation will be done in an iterative manner resulting in intermediate versions. The unique requirements of the healthcare development are already identified, and the development process will use them as a solid basis. Note that all data pertinent to the testing of healthcare APIs and applications will be a dummy; hence no ethical concerns will be raised. A diagnostic support module and the EHR interfacing will also be included.
Internet-based indoors localization technologies As it was aforementioned, in the ENDORSE project, localization solutions that will enable almost infrastructureless multi-robot navigation will be developed, so there will be the minimum installation of infrastructure (sensors and communications buses) on the building walls, ceilings and floors, reducing the cost and the time needed for infrastructure installation of indoor logistic systems. In parallel, the robustness of SLAM techniques used by ENDORSE will be enhanced, so that the navigation capabilities of the fleet will be fostered. Standard robot localization or location tracking approaches often rely on state estimation algorithms working in a stochastic framework; they usually show weak performance in the presence of data outliers, systematic errors or invalid model assumption.
Besides, most of these approaches require initial robot position to be known at least approximately; hence cannot solve the lost robot problem. We will develop a more robust and effective alternative, which is offered by set membership estimation and data fusion techniques that work in the bounded-error framework. These techniques can operate efficiently with sparse, asynchronous and heterogeneous measurements while being robust to the presence of non-consistent measurements, inaccuracy in environment modelling, and drift and inaccuracy in the robot evolution model. Furthermore, they can naturally track multi-hypotheses about robot pose, hence offer a solution technique for the robot global localization problem (Amri, Becis, Aubry, & Ramdani, 2015)  (Colle & Galerne, 2017). In ENDORSE project, a comparison and a selection of onboard sensors, that are employed for localization, will take place, in order to improve accuracy and stability of global localization (localization techniques fail, become less accurate in large spaces where Lidar or RGB-D have long travel distances) and reduce the computational burden of SLAM (which might result in deployment of less expensive hardware).
To this end, the fundamental tradeoffs will be identified on the requirements both on the type and nature of the sensors embedded or installed on the robot and on the environment. IMU will be considered for dead reckoning, cameras for a line or feature following and wheels encoders. Moreover, the use of RSSI measurements will be investigated (existing Wi-Fi beacons or deploy BLE or UWB). Ideally, only onboard sensors will be utilized and, thus, minimize installation time of ENDORSE system. However, alternative solutions, using barcodes or QR codes on the wall in well-chosen areas, will also be deployed, to make localization easier for the case of docking. As the indoor localization and mapping are concerned, effective data fusion and state estimation algorithms for indoor robust localization and mapping-update in the actual environment will be developed.
In other words, install the necessary interoperability mechanisms that will allow medical experts from different specialties, practising medicine in different hospitals, and using different systems, to connect to a patient's EHR and update it with the relevant clinical information. Within the context of the ENDORSE project, the objective is to augment our existing EHR system  (Antoniou et al. , 2017; EU Project, 2017)  complying with the afore-described interoperability requirements with vital signs data coming from the e-diagnostic support module detailed below. For that purpose, the necessary APIs will be developed relying on the Fast Healthcare Interoperability Resources ('Index -FHIR v3. 0. 1', 2018) standard, the most widely used standard for developing smartphone-based mobile health application today  (Avgousti et al.
As all the patients' medical data are instantly sent to a cloud-based server, this also limits human mistakes or loss of papers. The e-Diagnostic module offers a real service innovation providing several advantages regarding: • Organizational impact: the robot moves up to the medical caller (nurses, doctors) and not the opposite. The e-health module information collection provides diagnostic assistance for healthcare professionals, enabling faster and more efficient management of patients in all circumstances while optimizing the use of specialized human resources. • Economic impact: All hospitals have budget constraints. Instead of buying multiple medical devices for the same service, the e-Diagnostic module gathers all of them in one place allowing thus a gain of time and money. • Human impact: Days are already quite long and tedious in a hospital for medical staff having to run everywhere to find their tools.
It has been used for the analysis of the temporal rate of CMEs for instance by  Petrie (2013). The difference in peak rates suggests that some threshold has been changed resulting in a reduced rate by a factor of ≈ 2 in the second version, possibly to put it more in line with the CDAW occurrence rate. Unfortunately the two versions are not clearly distinguished (and the second version is not documented) thus introducing a confusion when the CACTus catalog is used. The second catalog further presents three lists labeled "CMEs", "Flows", and their combination. According to the website, "Flows" are "suspicious detections" generally being faint, narrow events. From April 1997 to April 2018, the data set comprises 19608 CMEs and 7693 flows; in broad terms, the former have larger speeds ( V = 480 km s -1 ) and larger widths ( W = 39 • ) than the flows ( V = 370 km s -1 , W = 18 • ).
For the time interval considered in this study, from March 1996 to September 2018 inclusive, the ARTEMIS-II (thereafter called ARTEMIS for simplicity) catalog lists a total of 39188 CMEs, of which 22894 have their velocities, mass, and kinetic energy determined. The ARTEMIS catalog is part of the LASCO-C2 Legacy Archive 5  hosted at the Integrated Data and Operation Center (formerly MEDOC) of Institut d'Astrophysique Spatiale. We conclude this presentation by an excerpt of the article of Wang and Colaninno (2014) concerning the ARTEMIS catalog: "In our view, the use of synoptic maps, which provide a relatively simple and clearly defined signature of CMEs in the form of vertical streaks, is more likely to yield consistent estimates of the long-term variation of CME rates than attempts to identify and distinguish outward-propagating ejections in individual images, even with the aid of automated techniques.
Likewise, we include a comparison with: (i) three photospheric indices or proxies of solar activity, sunspot number (SSN), sunspot area (SSA), and total photospheric magnetic flux (TMF) considered globally and by hemispheres; (ii) the GOES flare data and prominence data taken from three different sources: the Nobeyama Radioheliograph (NoRH) and Kislovodsk Observatory databases and the catalog compiled by  McCauley et al. (2015)  from observations with the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory restricted to the subset of unconfined events. More information on the data sources are given in Sects. 12. 2 and 14. For conciseness, we restrict the periodograms to those obtained with Fourier analysis and we do not present new wavelet spectra because of their similarity with those of  Barlyaeva et al.
This radiation is prominently produced in plasmas with electron temperatures of 10 6 K or more, henceforth typical of coronal temperatures, by free-free bremsstrahlung. We complete our selection by three photospheric indices/proxies: sunspot number (SSN), sunspot area (SSA), and total photospheric magnetic flux (TMF). The SSN data come from the WDC-SILSO data center, 8 and the SSA data from the RGO database. 9 The total photospheric magnetic flux (TMF), calculated from the Wilcox Solar Observatory photospheric field maps, was kindly made available to us by Y. -M. Wang; detail can be found in  Wang and Sheeley (2003). All indices are considered globally and, when available, by hemisphere to highlight any north-south asymmetry. Throughout this section, the CME and index/proxy data were sampled into 27.
Prominences from the Nobeyama Radioheliograph Database The Nobeyama Radioheliograph (NoRH) database 11 is based on the detection at 17 GHz of limb prominences thus insuring minimal projection effects. As a ground-based observatory, NoRH operates no more than 8 hr per day with further restrictions during the winters because 11 http://solar. nro. nao. ac. jp/norh/.
Prominences from the SDO/AIA Database The catalog of 1038 filament eruptions 13  is based on extreme ultraviolet observations by SDO/AIA  (McCauley et al. 2015). Although limited to slightly more than four years covering the maximum of SC 24 (April 2010 to September 2014), it has the advantage of being free of biases inherent to ground-based observations. We considered the restricted subset of 598 events that excludes confined and non-radial events to maximize the likelihood of asso- The catalog gives the start and the end detection times of the eruptions and we adopted the mid-point. The distributions of time difference and angular separation between filament eruptions and CMES are displayed in Fig. 74 ; their profiles led us to select the same criteria as for the NoRH EPs, namely a time interval of 0-5 hours and a range of angular separation of ±25 •.