Feature layer generated from running the Summarize Within solution. ESRI Experience Builder Regions were summarized within ESRI Experience Builder Regions - copy

Feature layer generated from running the Summarize Within solution. PA Survey Locations were summarized within PA Counties

Feature layer generated from running the Summarize Within solution. MinneapolisCoffeeShops_Address were summarized within Minneapolis_Neighborhoods

ArcGIS is a platform, and the platform is extending to the web. ArcGIS Online offers shared content, and has become a living atlas of the world. Ready-to-use curated content is published by Esri, Partners, and Users, and Esri is getting the ball rolling by offering authoritative data layers and tools.Specifically for Natural Resources data, Esri is offering foundational data useful for biogeographic analysis, natural resource management, land use planning and conservation. Some of the layers available are Land Cover, Wilderness Areas, Soils Range Production, Soils Frost Free Days, Watershed Delineation, Slope. The layers are available as Image Services that are analysis-ready and Geoprocessing Services that extract data for download and perform analysis.We've made large strides with online analysis. The latest release of ArcGIS Online's map viewer allows you to perform analysis on ArcGIS Online. Some of the currently available analysis tools are Find Hot Spots, Create Buffers, Summarize Within, Summarize Nearby. In addition, we've created Ready-to-use Esri hosted analysis tools that run on Esri hosted data. These are in Beta, and they include Watershed Delineation, Viewshed, Profile, and Summarize Elevation.

Feature layer generated from running the Summarize Within solution. GLOBEObserver_TreeHeights_2019Mar20_to2020Nov6 were summarized within Administrative Forest Boundaries

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

Feature layer generated from running the Summarize Within solution. MinneapolisCoffeeShops_Address were summarized within Minneapolis_Neighborhoods

PubMed Article Summarization Dataset

PubMed Summarization Dataset

By ccdv (From Huggingface) [source]

About this dataset

The dataset consists of multiple files, including validation.csv, train.csv, and test.csv. Each file contains a combination of articles and their respective abstracts. The articles are sourced directly from PubMed, ensuring they represent a wide range of topics across various scientific disciplines.

In order to provide reliable datasets for different purposes, the files have been carefully curated to serve specific functions. validation.csv contains a subset of articles with their corresponding abstracts that can be used for validating the performance of summarization models during development. train.csv features a larger set of article-abstract pairs specifically intended for training such models.

Finally, test.csv serves as an independent evaluation set that allows developers to measure the effectiveness and generalizability of their summarization models against unseen data points. By using this test set, researchers can assess how well their algorithms perform in generating concise summaries that accurately capture the main findings and conclusions within scientific articles.

Researchers in natural language processing (NLP), machine learning (ML), or any related field can utilize this dataset to advance automatic text summarization techniques focused on scientific literature. Whether it's building extractive or abstractive methods or exploring novel approaches like neural networks or transformer-based architectures, this rich dataset provides ample opportunities for experimentation and progress in the field.

How to use the dataset

Introduction:

Dataset Structure:

• article: The full text of a scientific article from the PubMed database (Text).
• abstract: A summary of the main findings and conclusions of the article (Text).

Using the Dataset: To maximize the utility of this dataset, it is important to understand its purpose and how it can be utilized:

• Training Models: The train.csv file contains articles and their corresponding abstracts that can be used for training summarization models or developing algorithms that generate concise summaries automatically.

• Validation Purposes: The validation.csv file serves as a test set for fine-tuning your models or comparing different approaches during development.

• Evaluating Model Performance: The test.csv file offers a separate set of articles along with their corresponding abstracts specifically designed for evaluating the performance of various summarization models.

Tips for Utilizing the Dataset Effectively:

• Preprocessing: Before using this dataset, consider preprocessing steps such as removing irrelevant sections (e.g., acknowledgments, references), cleaning up invalid characters or formatting issues if any exist.

• Feature Engineering: Explore additional features like article length, sentence structure complexity, or domain-specific details that may assist in improving summarization model performance.

• Model Selection & Evaluation: Experiment with different summarization algorithms, ranging from traditional extractive approaches to more advanced abstractive methods. Evaluate model performance using established metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation).

• Data Augmentation: Depending on the size of your dataset, you may consider augmenting it further by applying techniques like data synthesis or employing external resources (e.g., pre-trained language models) to enhance model performance.

Conclusion:

Research Ideas

• Textual analysis and information retrieval: Researchers can use this dataset to analyze patterns in scientific literature or conduct information retrieval tasks. By examining the relationship between article content and its abstract, researchers can gain insights into how different sections of a scientific paper contribute to its overall summary.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description ...

Reporting polygons were created to display and quantify overburden material above the Mahogany bed, by PLSS township, in the Uinta Basin, Utah and Colorado as part of a 2009 National Oil Shale Assessment.

Reporting polygons were created to display and quantify overburden material above the Mahogany Zone, by PLSS section, in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment.

Feature layer generated from running the Summarize Within solution. Hennepin County Hazzards1 were summarized within One_Mile_from_SLP

CCDV Arxiv Summarization Dataset

Arxiv Summarization Dataset for CCDV

By ccdv (From Huggingface) [source]

About this dataset

The validation.csv file contains a set of articles along with their respective abstracts that can be used for validating the performance of summarization models. This subset allows researchers to fine-tune their models and measure how well they can summarize scientific texts.

The train.csv file serves as the primary training data for building summarization models. It consists of numerous articles extracted from the Arxiv database, paired with their corresponding abstracts. By utilizing this file, researchers can develop and train various machine learning algorithms to generate accurate summaries of scientific papers.

Lastly, the test.csv file provides a separate set of articles with accompanying abstracts specifically intended for evaluating the performance and effectiveness of summarization models developed using this dataset. Researchers can utilize this test set to conduct rigorous evaluations and benchmark different approaches in automatic document summarization.

With columns labeled as article and abstract, each corresponding to multiple repetitions in order to allow detailed analysis or multiple variations if required by users (e.g., different proposed summaries), this dataset provides significant flexibility in developing robust models for summarizing complex scientific documents.

How to use the dataset

• Introduction:

• File Description:

• validation.csv: This file contains articles and their respective abstracts that can be used for validation purposes.

• train.csv: The purpose of this file is to provide training data for summarizing scientific articles.

• test.csv: This file includes a set of articles and their corresponding abstracts that can be used to evaluate the performance of summarization models.

• Dataset Structure: The dataset consists of multiple columns, including article, article, abstract, abstract, article, article, abstract, abstract, article, and article columns.

• Usage Examples: This dataset can be utilized in various ways:

a) Training Models: You can use the train.csv file to train your own model for summarizing scientific articles from the Arxiv database. The article column provides the full text of each scientific paper, while the abstract column contains its summary.

b) Validation: The validation.csv file allows you to validate your trained models by comparing their generated summaries with the provided reference summaries in order to assess their performance.

c) Evaluation: Utilize the test.csv file as a benchmark for evaluating different summarization models. Generate summaries using your selected model and compare them with reference summaries.

• Evaluating Performance: To measure how well your summarization model performs on this dataset, you can use metrics like ROUGE (Recall-Oriented Understudy for Gisting Evaluation). ROUGE measures overlap between generated summaries and reference summaries based on n-gram co-occurrence statistics.

Note: Please ensure that you do not include any dates in your guide or refer specifically to any particular versions/examples within this dataset as it may require regular updates/revisions

Research Ideas

• Summarizing scientific articles: This dataset can be used to train and evaluate summarization models for the task of generating concise summaries of scientific articles from the Arxiv database. Researchers can utilize this dataset to develop novel techniques and approaches for automatic summarization in the scientific domain.
• Information retrieval: The dataset can be used to enhance search engines or information retrieval systems by providing concise summaries along with the full text of scientific articles. This would enable users to quickly grasp key information without having to read the entire article, improving accessibility and efficiency.
• Text generation research: Researchers interested in natural language processing and text generation can use this dataset as a benchmark for developing new models and algorithms that generate coherent, informative, and concise summaries from lengthy scientific texts. The dataset provides a diverse range of articles across various domains, allowing researchers to explore different challenges in summary generation

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain...

Feature layer generated from running the Summarize Within solution. Poverty_by_Census_Tract_2 were summarized within City Council District Outlines

Feature layer generated from running the Summarize Within solution. Poverty_by_Census_Tract_2 were summarized within City Council District Outlines

IntroductionText summarization is a longstanding challenge in natural language processing, with recent advancements driven by the adoption of Large Language Models (LLMs) and Small Language Models (SLMs). Despite these developments, issues such as the “Lost in the Middle” problem—where LLMs tend to overlook information in the middle of lengthy prompts—persist. Traditional summarization, often termed the “Stuff” method, processes an entire text in a single pass. In contrast, the “Map” method divides the text into segments, summarizes each independently, and then synthesizes these partial summaries into a final output, potentially mitigating the “Lost in the Middle” issue. This study investigates whether the Map method outperforms the Stuff method for texts that fit within the context window of SLMs and assesses its effectiveness in addressing the “Lost in the Middle” problem.MethodsWe conducted a two-part investigation: first, a simulation study using generated texts, paired with an automated fact-retrieval evaluation to eliminate the need for human assessment; second, a practical study summarizing scientific papers.ResultsResults from both studies demonstrate that the Map method produces summaries that are at least as accurate as those from the Stuff method. Notably, the Map method excels at retaining key facts from the beginning and middle of texts, unlike the Stuff method, suggesting its superiority for SLM-based summarization of smaller texts. Additionally, SLMs using the Map method achieved performance comparable to LLMs using the Stuff method, highlighting its practical utility.DiscussionBoth theoretical and practical studies suggest that using Map method for summarization with SLM allowed to address the “Lost in the Middle” problem and outperform Stuff method.

Feature layer generated from running the Summarize Within solution. GSI_mpls_0015 were summarized within Gentrification Status

Vietnamese Multiple Document Summarization Dataset

ViMs Dataset (ViMs folder)

This work was supported by the Ho Chi Minh City Department of Science and Technology, Grant Numbers 15/2016/HÐ-SKHCN

Data construction process: In this work, we aim to have 300 clusters of documents extracted from news. To this end, we made use of the Vietnamese language version of Google News. Due to the copyright issue, we did not collect articles from every source listed on Google News, but limited to some sources that are open for research purposes. The collected articles belong to five genres: world news, domestic news, business, entertainment, and sports. Every cluster contains from four to ten news articles. Each article is represented by the following information: the title, the plain text content, the news source, the date of publication, the author(s), the tag(s) and the headline summary.

After that, two summaries are created for each cluster (produced in the first subtask above) by two distinguished annotators using the MDSWriter system (Meyer, Christian M., et al. "MDSWriter: Annotation tool for creating high-quality multi-document summarization corpora." Proceedings of ACL-2016 System Demonstrations). These annotators are Vietnamese native speakers and they are undergraduate students or graduate students. Most of them know about natural language processing. The full annotation process consists of seven steps that must be done sequentially from the first to the seventh one.

Data information: Original folder: Containing 300 subdirectories which are 300 news clusters. Articles (documents) in each cluster belong to a similar topic and there are from four to ten of them. The number of articles is 1,945.

Summary folder: Contains 300 subdirectories which are 600 final summaries. Every input cluster has two manual abstract summaries from two distinguished annotators. ViMs can be used for both implementing and evaluating supervised machine learning-based systems for Vietnamese abstractive multi-document summarization.

S3_summary folder: Contains 300 subdirectories including 600 ''best sentence selection'' summaries, the result of step 3 -- best sentence selection step. Sentences in a group are separated from others by a blank line. The most important sentence is marked as 1 while 0 is the label for others.

@article{tran2020vims, title={ViMs: a high-quality Vietnamese dataset for abstractive multi-document summarization}, author={Tran, Nhi-Thao and Nghiem, Minh-Quoc and Nguyen, Nhung TH and Nguyen, Ngan Luu-Thuy and Van Chi, Nam and Dinh, Dien}, journal={Language Resources and Evaluation}, volume={54}, number={4}, pages={893--920}, year={2020}, publisher={Springer} }

Vietnamese MDS (clusters folder)

Author: Tran Mai Vu, Vu Trong Hoa, Phi Van Thuy, Le Duc Trong, Ha Quang Thuy Affiliation: Knowledge Technology Laboratory, University of Technology, VNU Hanoi Research Topic: Design and Implementation of a Multi-document Summarization Program for the Vietnamese Language, Funded by the Ministry of Education (Project Code: B2012-01-24)

Data construction process: The data construction process is entirely manual. It consists of two steps:

• Step 1: Data Collection and Clustering - Data is collected from the Baomoi website and organized into clusters, where each cluster contains documents related to a specific topic. The data is collected from various subjects on Baomoi, typically encompassing 8-10 main categories, such as World, Society, Culture, Economics, Science-Technology, Sports, Entertainment, Law, Education, Health, Automobiles, and Real Estate.
• Step 2: Summarization - Two individuals participate in creating reference summaries. The summarization process includes two stages: extracting important sentences and rewriting them into a coherent paragraph.

Data information: Data Volume: 200 clusters

Each cluster corresponds to a folder, and it typically contains 2-5 documents (often 3). The folder's name represents the cluster.

Within each folder:

• .info: Contains cluster ID and cluster label (labels are assigned by the cluster creator based on content).
• .ref1.txt: Reference summary created by summarizer 1.
• .ref1.tok.txt: Tokenized version of reference summary 1, with sentences and words separated.
• .ref2.txt: Reference summary created by summarizer 2.
• .ref2.tok.txt: Tokenized version of reference summary 2, with sentences and words separated.
• .sum.txt: Machine-generated summary.
• .sum.tok.txt: Tokenized version of the machine-generated summary, with sentences and words separated.

All files within the same folder represent documents (online articles) belonging to the cluster:

• .body.txt: Contains the main content of the document.
• .body.tok.txt: Contains the document's content with sentences and words separated.
• .info.txt: Contains other in...

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

The tree canopy data was derived from the ArcGIS Living Atlas USA NLCD Tree Canopy Cover raster layer, which represents the canopy cover percentage for each 30-meter size raster cell. The granular raster data was rolled up to the census tract level by averaging the values for all the raster cells within each census tract (Ave Percent Tree Canopy Coverage). This was done with the Zonal Statistics as Table tool, similarly to the Air Quality workflow you saw earlier. The average percent tree canopy values were then used to determine the square kilometer coverage of tree canopy within each census tract (Total Canopy Coverage in Sq Km).Traffic data was also acquired from ArcGIS Living Atlas. The USA Traffic Counts point data was rolled up to the census tract for the city of Los Angeles (with the Summarize Within tool).

This dataset comes from the biennial City of Tempe Employee Survey question about feeling safe in the physical work environment (building). The Employee Survey question relating to this performance measure: “Please rate your level of agreement: My physical work environment (building) is safe, clean & maintained in good operating order.” Survey respondents are asked to rate their agreement level on a scale of 5 to 1, where 5 means “Strongly Agree” and 1 means “Strongly Disagree” (without “don’t know” responses included).The survey was voluntary, and employees were allowed to complete the survey during work hours or at home. The survey allowed employees to respond anonymously and has a 95% confidence level. This page provides data about the Feeling Safe in City Facilities performance measure. The performance measure dashboard is available at 1.11 Feeling Safe in City FacilitiesAdditional InformationSource: Employee SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: CSVPreparation Method: Data received from vendor and entered in CSVPublish Frequency: BiennialPublish Method: ManualData Dictionary (update pending)