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79 results for “Summarization”
Daily summarized seasonal measurements of discharge, water temperature, and specific conductivity from the Onyx River at Lower Wright, McMurdo Dry Valleys, Antarctica (1972-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Onyx River, located in the Wright Valley. Measurements commenced in 1972 and are ongoing. This dataset extends through the first half of the 2022-23 field season.
Daily summarized seasonal measurements of discharge, water temperature, and specific conductivity from Huey Creek at F2, McMurdo Dry Valleys, Antarctica (1990-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Huey Creek at F2, located in the Fryxell Basin of Taylor Valley. Measurements commenced during the 1990-91 austral summer and are ongoing. This dataset extends through the first half of the 2019-20 field season. The Huey at F2 control was buried by sediment and rock sometime before 29 Dec 2010 and the site was abandoned on 19 Jan 2011. An upstream location was established 28 Dec 2012. This dataset extends through the first half of the 2022-23 field season.
Soil Properties Summarized by Lots in Ouro Preto D'Oeste, Rondônia - Brazil (2019 surveyed lots V2)
<p>Soil properties data, summarized (weighted average by area with the given property on the lots surveyed on 2019 - V2) over small properties lots in Ouro Preto d'Oeste, Rondônia - Brazil:<br> AWHC:Available Water Holding Capacity: Water content at “field capacity” minus Water content at wilting point. Water content is % by weight. (50% = 50% of the weight of a lump of soil is water). (%)<br> PBS: Percent Base Saturation (%)<br> CLAY: Clay content (%)</p>
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
<p>This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. </p> <blockquote> <p>Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics.</p> </blockquote> <p>For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>).</p> <p><strong>The data is structured as follows:</strong></p> <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained <em>with the background section as input. </em>These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td><em>Results from Table 2</em></td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td><em>Results from Table 3</em></td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td><em>Results from Table 4</em></td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td><em>Results from Table 5</em></td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>
Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials
<p>Data extraction table for scoping review on best practices for co-creating patient-facing educational materials</p>
Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment
<p><strong>[EN] Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment.</strong></p> <ul> <li><strong>Description</strong>: MLSUM is a dataset of newspappers articles aimed at training summaring model. We use it for a constraints annotation experiment on newspapper titles according to their topic classification.</li> <li><strong>Content</strong>: For constraints annotation experiment based on data similarity, this dataset have been subsetted (randomly pick 75 articles in the following 14 most used topics: 'economie', 'politique', 'sport', 'planete' (renamed in 'ecologie'), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'educations', 'climat' (renamed in 'meteo'), 'immobilier') and filtered (keep articles that have an obvious topics regarding their titles, without their bodies). Two reviewers have working on this task in order to limit the subjectivity of the filtering. This subsetted dataset is used (1) to estimate needed time to annotate titles similarity with constraints (MUST-LINK, CANNOT-LINK) and (2) to test interactive clustering methodology (constraints annotation and constrained clustering).</li> <li><strong>Origin</strong>: The dataset is bassed on the original 'MLSUM: The Multilingual Summarization Corpus' dataset (https://doi.org/10.48550/arXiv.2004.14900).</li> </ul> <p><br> <strong>[FR] Echantillon de 'MLSUM: The Multilingual Summarization Corpus' pour une expérience d'annotation de contraintes.</strong></p> <ul> <li><strong>Description </strong>: MLSUM est un ensemble de données d'articles de journaux destinés à l'entraînement d'un modèle de résumé automatique. Nous l'utilisons pour une expérience d'annotation de contraintes sur des titres de journaux en fonction de leur classification thématique.</li> <li><strong>Contenu </strong>: Pour une expérience d'annotation de contraintes basée sur la similarité des données, cet ensemble de données a été échantillonné (sélectionner au hasard de 75 articles dans les 14 sujets les plus utilisés : 'économie', 'politique', 'sport', 'planète' (renommé en « écologie »). ), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'éducations', 'climat' (renommé en 'meteo'), 'immobilier' ) et filtré (conserver les articles qui ont un sujet évident par rapport à leur titre, sans leur corps). Deux relecteurs ont travaillé sur cette tâche afin de limiter la subjectivité du filtrage. Ce sous-ensemble de données est utilisé (1) pour estimer le temps nécessaire pour annoter la similarité des titres avec des contraintes (MUST-LINK, CANNOT-LINK) et (2) pour tester la méthodologie de clustering interactif (annotation de contraintes et clustering contraint).</li> <li><strong>Origine </strong>: L'ensemble de données est basé sur l'ensemble de données original 'MLSUM : The Multilingual Summarization Corpus' (https://doi.org/10.48550/arXiv.2004.1490).</li> </ul>
Daily summarized seasonal measurements of discharge, water temperature, and specific conductivity from Harnish Creek Tributary (Relict Channel) at F11, McMurdo Dry Valleys, Antarctica (1996-2020, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Harnish Creek Tributary (Relict Channel) at F11, located in the Fryxell Basin of Taylor Valley. Measurements commenced during the 1996-97 austral summer and are ongoing. This dataset extends through the first half of the 2019-20 field season.
SURF: Replication Package for: "What Would Users Change in My App? Summarizing App Reviews for Recommending Software Changes"
<p>Description of the content of folder "SURF_replication_package": 1) "Experiment I" contains: a) the folder "summaries" which contains all the html summaries generated through SURF and browsed by study participants involved in the Experiment I. b) the folder "XMLreviews" which contains, for each of the apps involved in the Experiment I, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input files for the SURF tool for generating the summaries contained in the "summaries" folder c) "Experiment_I_results.xlsx" which contains all the answers to our survey collected from the Experiment I participants.</p> <p>2) "Experiment II" contains: a) the folder "summaries" which contains the two html summaries generated through SURF and browsed by study participants in the Experiment II. b) the folder "XMLreviews" which contains, for each of the two apps involved in the Experiment II, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input of the SURF tool for generating the summaries contained in the "summaries" folder. c) "Experiment_II_results.xlsx" which contains all the user feedbacks extracted/validated by survey participants in the two sub-experiments. d) "Experiment_II_survey_answers.xlsx" which contains all the answers to our survey collected in the Experiment II participants.</p> <p>3) "Survey.pdf" which contains the pdf version of the survey performed by the participants</p> <p>4) "SURF_tool.zip" contains: a) "SURF.jar", which contains the class files of a prototypical implementation of SURF b) "README.txt" which contains the instructions to run the SURF tool c) the "lib" folder, which contains all the java libraries needed for running SURF.</p>
EduQuick: A Dataset for Assessing Summarization of Informal Educational Content for Social Media
<p>The presented dataset is a curated collection of model-generated text for educational TikTok content, abbreviated as EduQuick. This dataset is the result of evaluating and selecting high-quality content generated by the GPT-4 model following an empirical study. It aims to provide engaging and informative summaries suitable for TikTok's educational audience.</p>
Crimes in Chicago from 2001 - 2018 summarized per year
<p>Crimes in and around Chicago from the years 2001 - 2018 from data.gov - summarized to an annual sum</p> <p><a href="https://data.cityofchicago.org/">original publisher</a></p> <p><a href="https://data.cityofchicago.org/api/views/ijzp-q8t2">metadata</a></p> <p><a href="https://catalog.data.gov/dataset/crimes-2001-to-present-398a4">original dataset and description</a></p> <p> </p>
RepLab Summarization Dataset
<p><strong>RepLab Summarization Dataset</strong></p> <p>This package contains the dataset generated in the research published in the paper:</p> <p>"Javier Rodríguez-Vidal, Jorge Carrillo-de-Albornoz, Enrique Amigó, Laura Plaza, Julio Gonzalo and Felisa Verdejo. 2019. Automatic Generation of Entity-Oriented Summaries for Reputation Management. Ambient Intelligence & Humanized Computing."</p> <p>The dataset is available for research purpose. If you use it, please, cite us.</p> <p>This README file contains: </p> <p>1) A brief description of the corpus<br> 2) A description of the contents of each directory in this package.</p> <p><br> <strong><em>1. Description of RepLab Summarization Dataset</em></strong></p> <p><br> The RepLab summarization dataset contains companies data from the RepLab 2013 dataset (http://nlp.uned.es/replab2013/), where users from Twitter talk about different topics of the companies. <br> Each topic consists of a different number of tweets posted by Twitter users.</p> <p>The collection comprises tweets about 31 entities from two domains: automotive and banking. As a result, our subset of RepLab 2013 comprises 71,303 English and Spanish tweets</p> <p>For each entity, tweets are groupped in topics and for each topic three different summaries are manually generated: abstractive english, abstractive spanish and extractive.</p> <p>Please see the paper for further details.</p> <p> </p> <p><strong><em>2. Description of the contents of this package</em></strong></p> <p>./entities:</p> <p>This directory includes the information of each organization in order to create a summary. Each .xml file corresponds to an entity and includes the following information:</p> <p> -”Corpus entity”: Id of the entity.<br> -”cluster”: each one of the topics of the entity.<br> -"label": name of the topic.<br> -"priority": level of relevance of the topic: Alert (the highest priority being a reputation alert, i.e., an issue that requires an immediate response from the entity), Midly_important (relevant for the entity, an intermediate priority)<br> or unimportant (the lowest priority). <br> -”tweet”: Information about the tweets.<br> -"id": Id of the tweet.<br> -"date": When the tweet was written.<br> -"followers": Of the author of the tweet.<br> -"polarity": Of the tweet.<br> -"text": Text of the tweet.<br> -"summary": Information about the summary:<br> -"abstract_EN": Abstractive summary in English.<br> -"abstract_ES": Abstractive summary in Spanish.<br> -"tweet": Id of the tweet(s) selected for the extractive summary (if it is not filled, the extractive summary is the one of the tweets in the topic).</p>
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
A Compiled Archaeobiological Dataset for Central Asia's Chalcolithic through Bronze Age: macrobotanical and zooarchaeological data transcribed, standardized, and summarized from original publications
<p>This dataset contains archaeobotnaical and zooarchaeological data that have been compiled, transcribed, standardized, and summarized from original published data sources. Original data publications are given herein as a List of References (Microsoft Word file). These publications appeared between 1960-2022, presenting data in various formats, in various languages, and in scientific works that included journals, books, and conference proceedings - .</p> <p>Data have been compiled and are given in two Microsoft Excel files, one corresponding to archaeobotanical data and one to zooarchaeological data. On the first tab (worksheet) of each of these files, original publication sources are given in a summarized reference (Author, Year, Table/Figure Number) that corresponds to the full bibliographic reference in the accompanying List of References file. This first tab (worksheet) also summarizes additional information on the archaeological context of each dataset, collection and analysis methods (when reported), and the availabilty of other relevant and/or corresponding datasets. The remaining tabs (worksheets) in each file, organized alphabetically by author last name, tabularize the original data in a standardized format; these data are compiled and transcribed as necessary from the various formatting of original data sources, though they keep the original reported species names, table ordering, and numerical data. In the cases where totals were obviously erroneous or superceded by later or additional analyses, transcription notes have been offered directly in the worksheet.</p> <p>A fair number of these data sources are now out of print, have no digital distribution, and are otherwise difficult to access physically and/or linguistically. Accordingly, the sole aim in compiling these data here is to facilitate their widespread availablity, proper citation, and increased use within the international community of archaeological scientists working in Central Asia in the present day. Other scholars are encouraged to utilize these compiled datasets for foundational regional data and extended analyses, and to add to and expand these datasets going forward.</p>
Fig. 25. Canonical variate analysis summarizing craniodental differences between G. g in The Cross River Gorillas: A Distinct Subspecies, Gorilla gorilla diehli Matschie 1904
Fig. 25. Canonical variate analysis summarizing craniodental differences between G. g. diehli males (n = 20), G. g. diehli females (n = 13), G. g. gorilla males (n = 35) and G. g. gorilla females (n = 17) based on 11 measurements: (1)incisor row diameter, (2) bimastoid diameter, (3) bizygomatic diameter, (4) biglenoid diameter, (5) interparietal diameter, (6) M1 mesiodistal length, (7) biorbital diameter, (8) skull vault length, (9) cheek tooth row length, (10) P3 mesiodistal length, and (11) maximum palate width listed in decreasing order of discriminating ability. According to a stepwise discrimination analysis these measurements best summarize the measured differences between groups. Mahalanobis generalized squared distances (D2) are 15.52 between the two male means; 6.85 between the two female means; 29.61 between the male and female means for G.g. diehli, 39.09 between male and female means of G. g. gorilla, 47.94 between G. g. diehli females and G. g. gorilla males, and 39.44 between G. g. gorilla females and G. g. diehli males.
Fig. 59. Cladogram summarizing the 23 in A Review Of Dromaeosaurid Systematics And Paravian Phylogeny
Fig. 59. Cladogram summarizing the 23 alternate positions that Pyroraptor olympius can take among dromaeosaurids.
Companion data of Summarizing task-based applications behavior over many nodes through progression clustering
<p>This is the companion data for the paper *Summarizing task-based applications behavior over many nodes through progression clustering* by Lucas Leandro Nesi, Vinícius Garcia Pinto, Lucas Mello Schnorr, and Arnaud Legrand accept for publication in 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (<a href="https://www.pdp2023.org/">PDP 2023</a>). The remaining of the companion is at: https://gitlab.com/lnesi/companion-pdp-2023.</p>
MeetingBank: A Benchmark Dataset for Meeting Summarization
<p>MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,366 meetings with over 3,579 hours of video, as well as transcripts, PDF documents of meeting minutes, agenda, and other metadata. On average, a council meeting is 2.6 hours long and its transcript contains over 28k tokens, making it a valuable testbed for meeting summarizers and for extracting structure from meeting videos. The datasets contains 6,892 segment-level summarization instances for training and evaluating of performance.</p>
Summarizing the state of the terrestrial biosphere in few dimensions
<p>This repository contains the data and a docker container to reproduce the analysis in</p> <p>Kraemer, G., Camps-Valls, G., Reichstein, M., & Mahecha, M. D. (2020). Summarizing the state of the terrestrial biosphere in few dimensions. <em>Biogeosciences</em>, <em>17</em>(9), 2397–2424. <a href="https://doi.org/10.5194/bg-17-2397-2020">https://doi.org/10.5194/bg-17-2397-2020</a></p> <p>If you use this data and/or code, please cite the paper.</p> <p>This dataset is a subset of the DataCube v1.0.0 (https://www.earthsystemdatacube.net) plus a the FAPAR_tip dataset from DataCube v1.0.2</p> <p>The code to reproduce the analysis can also be found in http://doi.org/10.5281/zenodo.3733783 and https://github.com/gdkrmr/summarizing_the_state_of_the_biosphere</p> <p>To run the docker container, import the container, extract the data, run the command below, and open your web browser to access the jupyter server. Modify the command below as necessary, for details please consult the documentation for docker and jupyter:</p> <p>PORT=8333<br> IP=0.0.0.0<br> docker run -it \<br> -v /path/to/data/low-res/:/home/science/summarizing_biosphere/data \<br> -v /path/to/results_and_temporary_files/:/home/science/summarizing_biosphere/results \<br> -p $PORT:$PORT \<br> summarizing_biosphere \<br> /home/science/.julia/conda/3/bin/jupyter \<br> notebook \<br> --port=$PORT \<br> --ip=$IP \<br> --no-browser</p> <p> </p> <p> </p>
Dataset for summarization of microblogs
<p>This is the dataset used in the following paper:<br> Soumi Dutta, Vibhash Chandra, Kanav Mehra, Asit Kumar Das, Tanmoy Chakraborty, Saptarshi Ghosh. Ensemble Algorithms for Microblog Summarization. IEEE Intelligent Systems (Special Issue on Summarization of Things), vol. 33, no. 3, pp. 4--14, May/June 2018. </p> <p>The dataset contains four sets of tweets posted during four different disaster events, and summaries written by three human annotators for each tweet-set. The dataset can be used for evaluating algorithms for microblog summarization. </p> <p>The included README file giving a detailed description of the dataset. </p>
Appendix_results_qual_analysis_summarized (42-language sample)
<p>A .pdf file that plots verb scores (1 to 3) in main and adverbial clauses in the sample languages covered by the CIEP (42-language sample).</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.