dataesr/scientific-paragraphs-categorization huggingface dataset:
We use two types of codes as follows: 1) Metadata control tokens. Those are special tokens that are associated with each input review, and are the capitalized control tokens in Fig. 1. We use two types of metadata that represent (i) the review polarity, a numerical value denoting the average sentiment score of the input reviews; (ii) and categorical tokens representing the type of the entity of the review (e. g. Deli, Beauty&Spa, Furniture Stores). In the case of the unavailability of meta-data labels for all reviews (as in Rotten-Tomatoes dataset), we infer control tokens with the same process, but using categories predicted by a trained classifiers on labeled examples from the same domain. 2) Inferred control tokens. We follow recent work  (Keskar et al. , 2019; Dathathri et al. , 2019 ) that shows that it is preferable to condition NLG models on control tokens that naturally co-occur in text.
All models were trained on one machine with 4 NVIDIA V100 GPUs, the longest model took 50 hours to train. For inference, we use a beam size of 35. We discard hypotheses that contains twice the same trigram. We limit generation of each summary to a maximum budget of 150 tokens for each summary for Yelp, as was done by  Chu & Liu (2019) , and a budget of 50 tokens for Rotten Tomatoes. We set a similar budget for all other extractive baselines in the experiments. Finally, we use length normalization  (Wu et al. , 2016)   Datasets We evaluate our proposal on two English datasets: Yelp 2  (Chu & Liu, 2019)  and Rotten Tomatoes  (Wang & Ling, 2016). The Yelp dataset contains reviews of businesses (approximately one million reviews for around 40k venues). As described in Section 3, for each venue, we select the best reviews to use as target summaries: either the top-p (with p = 15%) or the top-T (with T = 100) reviews, whichever is smaller.
For each target summary thus selected, we then take its k = 8 most similar reviews (cosine similarity) to form its input. We obtain around 340k training examples, representing 22. 5k venues. The Rotten Tomatoes dataset was constructed by  (Wang & Ling, 2016)  from the movie review website rottentomatoes. com. We use the same process as for Yelp, but use p = 1% and T = 150. We construct around 170k training examples, representing 3. 7k movies. Details about dataset sizes and splits are in the Appendix.
Evaluation Metrics We evaluate summary systems with the classical ROUGE-F-{1,2,L} metrics  (Lin, 2004). 3 We also report BERT-score  (Zhang et al. , 2020) , a metric that uses pre-trained BERT  (Devlin et al. , 2019)  to compute the semantic similarity between a candidate summary and the 2 https://www. yelp. com/dataset/challenge 3 For Yelp we use the MeanSum  (Chu & Liu, 2019)  implementation to keep results comparable while for RottenTomatoes we use py-rouge package pypi. python. org/pypi/pyrouge/ 0. 1. 3 gold summary. Dist-n and Dist c -n (n = 1, 2, 3) scores  (Li et al. , 2016a)  are the percentage of distinct n-grams in the generated text on the summary level or the corpora level respectively. Dist-n is an indicator of repetitiveness within a single summary while Dist c -n indicates the diversity of different generations.
Finally, as done by  Chu & Liu (2019) , we use a classifier to check whether the sentiment of the summary is consistent with the sentiment of input reviews (Sentiment Acc. in Table  1 ). 4 We extend this method to check whether the correct product category can also be inferred from the summary, we report F category the micro F-score of the multi-label category classifier. Baselines and Other Systems We compare our system to three unsupervised baselines. TextRank  (Mihalcea & Tarau, 2004)  and LexRank  (Radev et al. , 2004)  are extractive systems based on the PageRank algorithm. Opinosis  (Ganesan et al. , 2010)  is an abstractive graph-based system. We use openly available Python implementations for TextRank 5  (Barrios et al. , 2016)  and LexRank. 6 We use the default parameters of the implementations.
TextRank (Extractive): Will not return This place is always good, I think the owner actually made my sandwich last time I was there , owner or manager, anyway it was superb! Ordered a sandwich, watched the guy write it down and 25 minutes later the same person asked what I wanted when I reminded him of my sandwich, he only said he can't remember where the order went. I watched 4 people come in after me order, one person the same sandwich just a different size then me get their food, pay and leave. At that point I gave up because as much as I like their sandwiches I am never going back. the official Java implementation 7  with default hyperparameters. 8 e also compare our systems with more recent neural unsupervised summarization systems. For the Yelp dataset, we rerun the released pretrained version of MeanSum 9   (Chu & Liu, 2019).
Evaluation Results Automatic Evaluation Table  1  contains the automatic evaluation metrics with respect to reference summaries. The proposed multi-input self-supervised model with control codes perform consistently better in the Yelp dataset across the benchmarked models, inlcuding the recent neural unuspervised models of MeanSum and H-VAE. Note that be-Correct Control Tokens: eat, lentil, eggplant, new, remember, flavourful, friendly Seriously best we've had in Toronto. We were looking for a new place to eat and stumbled upon this place. The atmosphere is very authentic, the food is authentic, and the service is very friendly and attentive. We started with the lentil soup, which was very flavourful and full of flavor. For dinner, I had the lamb shank and my husband had the eggplant dish.
10 For MeanSum we re-run their provided checkpoint and run evaluation through the same pipeline. The BERTScore  (Zhang et al. , 2020)  differences are closer and seem to favour neural models. With the RottenTomatoes dataset we only benchmarked the graph-based unsupervised methods, since the released pretrained MeanSum model does not cover the domain of. Proportion of control tokens fed as prompts that occur in the generated summary, for the setting of correct and incorrect control tokens. When the model is fed control tokens that occur in the input reviews (correct) it tends to generate them in the output. This is not the case when it is fed incorrect control tokens: it mostly ignores them. movie reviews. We attribute the lower score in sentiment accuracy to the fact that the "summaries" in RottenTomatoes are critical reviews, written in a very different style than the original reviews.
Technologies based on non-equilibrium, low-temperature plasmas are ubiquitous in today's society. Plasma modeling plays an essential role in their understanding, development and optimization. An accurate description of electron and ion collisions with neutrals and their transport is required to correctly describe plasma properties as a function of external parameters. LXCat is an open-access, web-based platform for storing, exchanging and manipulating data needed for modeling the electron and ion components of non-equilibrium, low-temperature plasmas. The data types supported by LXCat are electron-and ion-scattering cross-sections with neutrals (total and differential), interaction potentials, oscillator strengths, and electron-and ion-swarm/transport parameters. Online tools allow users to identify and compare the data through plotting routines, and use the data to generate swarm parameters and reaction rates with the integrated electron Boltzmann solver.
The LXCat project aims to address the latter issue-availability of reliable data-and more broadly to be a platform for the exploration and curation of data. LXCat is an open-access, web-based platform (http://www. lxcat. net) for storing, exchanging and manipulating data needed for modeling the electron and ion components of non-equilibrium, low-temperature, plasmas. Modeling such plasmas requires extensive input data, the specific form of which depends on the model formulation, as described in Section 2. The initial impetus for the LXCat project was the desire to develop a convenient way of exchanging data for the electron component of LTPs. These are the key data needed for modeling LTPs, and while modeling groups have been generally willing to share data, there was no convenient means for doing so.
The emphasis at the beginning was on compilations of "complete" sets of electron-neutral (ground state) scattering cross-sections which are required for input to Boltzmann equation solvers or Monte Carlo simulations to determine the electron energy distribution function (eedf). The term "complete" is used here to imply that the cross-sections account for all the important electron energy and momentum loss processes for electron collisions (with more or less detail). Thus, the calculated transport and rate coefficients (integrals over the eedf) are consistent measurements in swarm experiments  [7]. Please note that complete cross-section sets can be used to describe the electron component of the plasma in more general situations. As discussed in Section 4, LXCat today includes other data types associated with electron collisions and transport in LTPs as well as data for modeling the ion component of LTPs.
LXCat is structured into individual databases containing data uploaded by individual contributors who retain control over their database. In some cases, the databases contain data generated by the contributors themselves, but in other cases, the data have been compiled by the contributors from the literature. In all cases, references are provided, and all users are urged to cite those references in publications making use of data downloaded from LXCat. LXCat does not provide recommendations, but intercomparisons of some data sets have been performed under the auspices of the GEC (Gaseous Electronics Conference) Plasma Data Exchange project  [8]. Three papers have resulted from these efforts  [6, 9, 10]  with a focus on the electron-noble gas scattering cross-sections available at that time.
In contrast, obtaining similar data from LXCat is as simple as selecting the type of data desired, one (or more) of the data sets containing the desired type of data, and acknowledging the need for proper citation. There is no need to dig through physical volumes or to infer numerical values from tiny figures in a crooked scan. Not only does LXCat facilitate access to historical data, but it enables broader access to data that is not easily published in typical venues. For example, large data sets such as those produced by the B-spline R-matrix  [13]  method are such that only a fraction can be presented in a typical journal article. Though journals have been making progress in allowing for the inclusion of supplementary material, there is no mechanism to ensure that data published in such a manner can be easily read in by plasma modeling tools or compared to other data sets.
In contrast, LXCat, by storing data sets under a common template, encourages reuse and accessibility. Example of a "traditional way" of retrieving data from a physically printed plot of a paper by Biberman et al. [14]  which was itself scanned in the present case from a printed journal volume (courtesy from J. van Dijk, TU/e). Accessible LTPs modeling data are key to reproducible studies and facilitate fast and efficient advances in science. As stated in an editorial for Nature Physics  [15] : Apart from the obvious advantages that open data could bring for reproducibility and scientific integrity-although there is devil in the detail of how to implement this-one argument in favour of data sharing is that having more pairs of eyes on data may lead to new discoveries and faster scientific progress.
Such considerations have always driven the LXCat project and the associated development of online tools to analyze and compare data. More recently, these considerations have led to the development of a Time Machine feature which acknowledges the need for modeling data to evolve while maintaining access to historical values. In this paper, we will start with a brief discussion of the data needs for modeling low-temperature, non-equilibrium plasmas. Then we continue with the history of LXCat, review the status of the project while giving insight on the diversity of the data available on the website and operations that can be done with the data (i. e. , use of a Boltzmann solver to compute swarm parameters) and finish with a discussion of recent work and potential future developments. The present paper particularly aims to give a tutorial introduction to the types of data present on LXCat, to serve as a guide to new users of the data and website while providing some useful tips and underlining most common pitfalls.
Data Needs for Modeling LTPs A plasma is composed of positive and negative charged particles (electrons and ions) as well as neutral species which have several internal degrees of freedom. LXCat is focused on data for non-equilibrium LTPs. These plasmas are weakly ionized with the ionization degree (ratio of electron to total particle number densities) often smaller than 10 -4. Thus, the most numerous particles are background gas particles in the ground state. These plasmas are also typically far from local-thermal equilibrium with electrons possessing much higher translational energy than the other species in the gas phase. This can be attributed to the fact that only charged particles are subject to acceleration by an applied electric field and electrons are inefficient in transferring kinetic energy to the much more massive atoms and molecules  [16].
Data needs for modeling transport properties of plasmas in thermal equilibrium are beyond the present scope of LXCat. From this short discussion, one can see that in contrast to other fields of research such as combustion or atmospheric chemistry, the non-equilibrium nature of LTPs places special requirements on modeling data. Only a handful of extremely detailed state-to-state data sets (mostly for noble gases) can make plausible claims to cover most LTP conditions  [36]. However, it can be difficult to validate these datasets and added complications or concerns can limit their use. More frequently, one must use data sets with lumped transitions and effective states while exercising caution about the limitations of these models. Specifically, many such sets are developed with particular plasma models in mind and the individual cross-sections are not intended to be used outside of that context.
Origins The LXCat project was originally developed by researchers at LAPLACE, a CNRS laboratory on the campus of the University of Toulouse III. The vision for the LXCat project came from Sergey Pancheshnyi in 2007, and by 2008 he had a working version of the website with the compilations of data used in-house by the group GREPHE at LAPLACE. The first international presentation of the LXCat project was made at the 2010 ICAM-DATA meeting in Vilnius, Lithuania. At that time, there were six databases containing complete sets of cross-sections for electron-neutral interactions covering a total of 34 different neutral species explicitly intended to be used with two-term Boltzmann solvers. At that meeting, Prof. I. Bray from Perth, Australia asked about including results from his CCC calculations on LXCat.
Though the results of these calculations feature meaningful differences from the data sets designed for use with a Boltzmann solver, a decision was made to include them because of their physical relevance. The result of this was the CCC database containing results from the group in Perth. Over time, the number of databases on LXCat has grown both in those designed for Boltzmann solvers and those that are not. In October 2010, the GEC hosted a public discussion session on what was called the "GEC Plasma Data Exchange Project" where the LXCat website was presented for the first time and a call was made for participation from the larger LTPs community to make data available online. Following this discussion, the GEC initiated a series of evening workshops that have been held at the GEC almost every year.
In 2011, Prof. L. Viehland from Chatham University in Pennsylvania joined the list of contributors with his large collection of data related to ion-neutral collisions. A second website (ICECat)  [37]  was created to host these data which include compilations of data published in reviews since the 1970s. Further data from Prof. A. V. Phelps and from Prof. J. d'Urquijo for ion-swarm parameters were also added to this new website. In 2013, for technical reasons, it was decided to merge LXCat and ICECat into a single, larger project which would retain the name, LXCat. This structure has endured and both electron and ion data are now available through the same interface. Although the number and type of interactions provided by LXCat are greater than ever, the backend has remained essentially unchanged.
LXCat 10 Years Later LXCat has grown into an international project with contributors from 4 different continents as illustrated in Figure  2. The red markers on the map represent the locations of team members (including both the Tech and Outreach teams). The nations of the data set contributors are colored in blue, showing a broad coalition. Even this picture is somewhat conservative as the data that comprise any given data set have their own diverse origins. This can also be found in the extensive citations that accompany the data sets found on LXCat. A look at the website analytics reveals a similar picture-LXCat is a truly international project. A glance at Figure  3  illustrates the increasing and steady use of LXCat in the LTP community. The number of citations shows the extent of the project's impact with over 1200 citations in the scientific literature (note that multiple references within each paper citing LXCat are not counted here).
Contributors Figure  2. Geographic location of the countries currently contributing to the LXCat project. Please note that this map reflects on the location of direct contributors to the project and not necessarily on the countries from which the data originally came from. Although these plots show the clear increase in the number of users, the contributors activity has also been growing. The data sets are not static and are frequently updated by their respective contributors. This posed a challenge for the traceability of data downloaded from LXCat and used in publications which was solved by the development of the Time Machine function which provides access to historical data. Because of the dynamic nature of the data, citing the date of retrieval of any given data set is critical for the traceability of the data.
Data for Modeling of Plasmas (DMP) Association The LXCat team has received several queries from the individuals in the LTP community on how to support the project financially. To this end, the non-profit DMP association (https://assoc. lxcat. net/) was created in France with a mission "to facilitate the exchange of data and numerical tools for modeling the physics and chemistry of plasma. " Although it serves as the semi-formal organizing structure for LXCat, it possesses a much larger scope. Specifically, the association's objectives are to: Resources of the association so far include annual institutional membership dues, but public and private donations and grants are also possible.
What Is Available on LXCat? LXCat makes a wide range of data and tools available to its users. The data include many different types of charged particle interaction data that originate from multiple contributors. There are also data that are still being incorporated into the main framework of the database, but are made available in a "staging area", as-is. In terms of the tools, LXCat offers an online Boltzmann solver, as well as links and downloads for other plasma modeling tools that use its data as well as other software of interest to the LTPs community. In this section, we give a brief overview of the range of resources that can be found on LXCat and give more detail on additions that were not covered by the last LXCat review  [38].
List of Data Groups, Databases and Species An extensive review and description of the available databases on LXCat until June 2016 was provided by Pitchford et al. [38]. We therefore provide here only a concise overview of the existing databases and discuss only in more detail notable changes and recent new contributions. In the following, excepted for Figure  4 , we make the choice to present data "as-is" after generation by the LXCat online tools to illustrate the functionalities of the website.
Differential Scattering Cross-Sections Currently, LXCat contains differential scattering cross-sections of electrons with neutral species in 4 databases: BSR  [41, 42] , CCC  [44] [45] [46] [47] , Christophorou (for SF 6  [48]  and CF 3 I  [49] ), and ANU. The differential cross-section data in the CCC database are new since 2016 and contains data for H 2  [47, 50]. The ANU database was introduced in October 2016, and contains a significant number of species, namely He  [51] , Ne [52], Ar  [39] , Kr  [53, 54] , Xe  [55] , CF 2  [56] , CH 4  [57] , C 2 F 4  [58] , C 2 H 4  [59] , C 4 F 8  [60]   [72] , N 2  [73, 74] , N 2 O  [75] , NH 3  [76, 77] , NO  [78] , and SO 2  [79]. The database is a compilation of experimental data from researchers at the Australian National University and their collaborators.
• Effective: momentum transfer including all electron-scattering with a given target particle species. This is equal to the sum of the elastic momentum transfer and the total cross-sections for all inelastic and ionization processes. For historical reasons, this is included instead of elastic momentum transfer in some data sets. For electron-scattering cross-sections with neutrals, there are in total 21 different databases with the following 18 already extensively discussed in  [38]  Additionally, the UBC database [119] contains oscillator strengths for scattering of electron with neutral species in their ground state as a function of the electron energy. For optically allowed transitions, such data allow an estimate of the cross-section for transition between two levels by electron impact.
A well-known approximation is the Drawin formula for excitation of an atom between the k and l-levels  [120]. In the case of electric dipole allowed transitions, oscillator strengths are also used quite successfully for rescaling plane-wave Born cross-sections for electron-impact excitation of neutral atoms  [121] [122] [123]. Two additional databases are currently under construction and already available online: the eMol-LeHavre database [124] and the Laporta database  [125]. The Le Havre group has calculated vibrationally and ion core resolved recombination cross-sections for various molecular ions and is planning to make them available on LXCat with crosssections for N + 2  [126]  and BeH +  [127, 128]  already uploaded. The Laporta database is a compilation of vibrationally resolved cross-sections for electron-molecule scattering based on R-Matrix calculations.
Swarm/Transport Data for Electrons There are 9 different databases for swarm and transport parameters of electrons in gases. The Christophorou  [95]  (data for SF 6 ) and IST-Lisbon [35] (data for Ar, CH 4 , H 2 , He, and N 2 ) databases also contain electron-scattering cross-sections and were already briefly described in Section 4. 1. 2. The following were already described in  [38]   [141], CF 3 -O-CF--CF 2 , HFC227ea [142], HFO1234ze [143], and the gas mix- tures C 3 HF 5 with CO 2 and N 2  [138]  Electron rate coefficients, which is a category of swarm parameters on LXCat, are available both as a function of reduced electric field and electron temperature which is typically defined as k B T e = 2/3 ¯ where ¯ is the electron mean energy. In many experiments such as the ones from Zhu et al.