Omkar2118/datasets huggingface dataset:
Telegraphic Summaries
Full name: Gold Corpus for Telegraphic Summarization

# README

	Created by Malireddy Chanakya & Srivenkata N Mounika Somisetty & Malireddy Chaitanya


## The dataset contains:

	- 200 short stories
	- 200 corresponding telegraphic summaries
	- 50 selected abstractive summaries
	- 50 selected extractive summaries each by SMMRY and RESOOMER
	- 45 MCQ questions for 15 stories (3 questions/story)
	- index.txt


### How to Use?

	index.txt contains a table listing out each:
    
		1.	story’s id
		2.	name
		3.	author
		4.	word count
		5.	length of the telegraphic summary
		6.	length of the abstractive summary

	Corresponding to each story an <id>.txt file is present in the stories directory, telegraphic directory and possibly in the abstractive directory


### Guidelines followed for telegraphic summaries:

	1. A segment is defined as a continuous span of words in the source, chosen as a part of the summary.
	2. A word should not be fragmented. Eg - if the word "breaking" appears in the source, the entire word should be a part of the segment and not fragments like "break".
	3. Each segment should be relevant to the plot, try to advance the story and have some continuity with the preceding and the following segment.
	4. Each segment extracted from a dialogue should be enclosed in quotes.
	5. Each segment extracted from parentheses should be enclosed in parentheses.
	6. Segments should be arranged in the same order as they appear in the story.
	7. The summary should be minimal. If multiple segments mean the same thing, pick the shortest. Adjectives, adverbs, and modifiers are not to be included if they are not 	relevant to the plot. Extraneous facts and long descriptions are to be ignored.
	8. When the segments are read in sequence the plot should be apparent and unambiguous.


### Guidelines followed for abstractive summarization:

	1. Summaries should be written from a third party perspective. Eg - "This story is about a girl..."
	2. Summaries should only discuss the plot and try to avoid inferences and opinions not immediately apparent from the story.
	3. Summaries should maintain the same order of events as they occur in the source text.

Introducing paper: Gold Corpus for Telegraphic Summarization
Visual Domain Decathlon
Full name: Visual Domain Decathlon

The goal of this challenge is to solve simultaneously ten image classification problems representative of very different visual domains. The data for each domain is obtained from the following image classification benchmarks:

ImageNet  
CIFAR-100  
Aircraft  
Daimler pedestrian classification  
Describable textures  
German traffic signs  
Omniglot  
SVHN  
UCF101 Dynamic Images  
VGG-Flowers  

The union of the images from the ten datasets is split in training, validation, and test subsets. Different domains contain different image categories as well as a different number of images.

The task is to train the best possible classifier to address all ten classification tasks using the training and validation subsets, apply the classifier to the test set, and send us the resulting annotation file for assessment. The winner will be determined based on a weighted average of the classification performance on each domain, using the scoring scheme described below. At test time, your model is allowed to know the ground-truth domain of each test image (ImageNet, CIFAR-100, ...) but, of course, not its category.
METR-LA Point Missing

The original dataset from [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926) contains traffic readings collected from 207 loop detectors on highways in Los Angeles County, aggregated in 5 minutes intervals over four months between March 2012 and June 2012.

The __Point missing__ setting, introduced in [Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks](https://arxiv.org/abs/2108.00298v3), is a variant for imputation in which 25% of data are masked out uniformly at random. Results on this dataset are assumed to be obtained __in-sample__, meaning that the test interval is used also for training, excluding data used for evaluation.

Introducing paper: Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
PEMS-BAY Point Missing

The original dataset from [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926) contains 6 months of traffic readings from 01/01/2017 to 05/31/2017 collected every 5 minutes by 325 traffic sensors in San Francisco Bay Area. The measurements are provided by California Transportation Agencies (CalTrans) Performance Measurement System (PeMS).

The __Point missing__ setting, introduced in [Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks](https://arxiv.org/abs/2108.00298v3), is a variant for imputation in which 25% of data are masked out uniformly at random. Results on this dataset are assumed to be obtained __in-sample__, meaning that the test interval is used also for training, excluding data used for evaluation.

Introducing paper: Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks