Datasets:

lara-martin
/

Scifi_TV_Shows

	---
	license: cc-by-4.0
	task_categories:
	- text-generation
	- text2text-generation
	language:
	- en
	tags:
	- story
	- storytelling
	- creative
	- summaries
	- TV
	- scifi
	pretty_name: Scifi TV Shows
	size_categories:
	- 100K<n<1M
	---

	# Dataset Card for Science Fiction TV Show Plots Corpus

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Format](#format)
	- [Using the Dataset with Hugging Face](#call-scifi)
	- [Original Dataset Structure](#dataset-structure)
	- [Files in _OriginalStoriesSeparated_ Directory](#original-stories)
	- [Additional Information](#additional-information)
	- [Citation](#citation)
	- [Licensing](#licensing)

	## Dataset Description
	A collection of long-running (80+ episodes) science fiction TV show plot synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story".

	Contains plot summaries from:
	- Babylon 5 (https://babylon5.fandom.com/wiki/Main_Page) - 84 stories
	- Doctor Who (https://tardis.fandom.com/wiki/Doctor_Who_Wiki) - 311 stories
	- Doctor Who spin-offs - 95 stories
	- Farscape (https://farscape.fandom.com/wiki/Farscape_Encyclopedia_Project:Main_Page) - 90 stories
	- Fringe (https://fringe.fandom.com/wiki/FringeWiki) - 87 stories
	- Futurama (https://futurama.fandom.com/wiki/Futurama_Wiki) - 87 stories
	- Stargate (https://stargate.fandom.com/wiki/Stargate_Wiki) - 351 stories
	- Star Trek (https://memory-alpha.fandom.com/wiki/Star_Trek) - 701 stories
	- Star Wars books (https://starwars.fandom.com/wiki/Main_Page) - 205 stories, each book is a story
	- Star Wars Rebels (https://starwarsrebels.fandom.com/wiki/Main_page) - 65 stories
	- X-Files (https://x-files.fandom.com/wiki/Main_Page) - 200 stories

	Total: 2276 stories

	Dataset is "eventified" and generalized (see LJ Martin, P Ammanabrolu, X Wang, W Hancock, S Singh, B Harrison, and MO Riedl. Event Representations for Automated Story Generation with Deep Neural Nets, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. for details on these processes.) and split into train-test-validation sets—separated by story so that full stories will stay together—for converting events into full sentences.

	---
	### Format
	\| Dataset Split \| Number of Stories in Split \| Number of Sentences in Split \|
	\| ------------- \|--------------------------- \|----------------------------- \|
	\| Train \| 1737 \| 257,108 \|
	\| Validation \| 194 \| 32,855 \|
	\| Test \| 450 \| 30,938 \|

	#### Using the Dataset with Hugging Face
	```
	from datasets import load_dataset

	#download and load the data
	dataset = load_dataset('lara-martin/Scifi_TV_Shows')
	#you can then get the individual splits
	train = dataset['train']
	test = dataset['test']
	validation = dataset['validation']
	```
	Each split has 7 attributes (explained in more detail in the next section):
	```
	>>> print(train)

	Dataset({
	features: ['story_num', 'story_line', 'event', 'gen_event', 'sent', 'gen_sent', 'entities'],
	num_rows: 257108
	})
	```

	---
	## Original Dataset Structure
	* File names: scifi-val.txt, scifi-test.txt, & scifi-train.txt
	* Each sentence of the stories are split into smaller sentences and the events are extracted.
	* Each line of the file contains information about a single sentence, delimited by "\|\|\|". Each line contains, in order:
	* The story number
	* The line number (within the story)
	* 5-tuple events in a list (subject, verb, direct object, modifier noun, preposition); e.g.,
	``
	[[u'Voyager', u'run', 'EmptyParameter', u'deuterium', u'out'], [u'Voyager', u'force', u'go', 'EmptyParameter', 'EmptyParameter'], [u'Voyager', u'go', 'EmptyParameter', u'mode', u'into']]
	``
	* generalized 5-tuple events in a list; events are generalized using WordNet and VerbNet; e.g.,
	``
	[['<VESSEL>0', 'function-105.2.1', 'EmptyParameter', "Synset('atom.n.01')", u'out'], ['<VESSEL>0', 'urge-58.1-1', u'escape-51.1-1', 'EmptyParameter', 'EmptyParameter'], ['<VESSEL>0', u'escape-51.1-1', 'EmptyParameter', "Synset('statistic.n.01')", u'into']]
	``
	* original sentence (These sentences are split to contain fewer events per sentence. For the full original sentence, see the OriginalStoriesSeparated directory.); e.g.,
	``
	The USS Voyager is running out of deuterium as a fuel and is forced to go into Gray mode.
	``
	* generalized sentence; only nouns are generalized (using WordNet); e.g.,
	``
	the <VESSEL>0 is running out of Synset('atom.n.01') as a Synset('matter.n.03') and is forced to go into Synset('horse.n.01') Synset('statistic.n.01').
	``
	* a dictionary of numbered entities by tag within the _entire story_ (e.g. the second entity in the "<ORGANIZATION>" list in the dictionary would be <ORGANIZATION>1 in the story above—index starts at 0); e.g.,
	``
	{'<ORGANIZATION>': ['seven of nine', 'silver blood'], '<LOCATION>': ['sickbay', 'astrometrics', 'paris', 'cavern', 'vorik', 'caves'], '<DATE>': ['an hour ago', 'now'], '<MISC>': ['selected works', 'demon class', 'electromagnetic', 'parises', 'mimetic'], '<DURATION>': ['less than a week', 'the past four years', 'thirty seconds', 'an hour', 'two hours'], '<NUMBER>': ['two', 'dozen', '14', '15'], '<ORDINAL>': ['first'], '<PERSON>': ['tom paris', 'harry kim', 'captain kathryn janeway', 'tuvok', 'chakotay', 'jirex', 'neelix', 'the doctor', 'seven', 'ensign kashimuro nozawa', 'green', 'lt jg elanna torres', 'ensign vorik'], '<VESSEL>': ['uss voyager', 'starfleet']}
	``

	### Files in _OriginalStoriesSeparated_ Directory
	* Contains unedited, unparsed original stories scraped from the respective Fandom wikis.
	* Each line is a story with sentences space-separated. After each story, there is a <EOS> tag on a new line.
	* There is one file for each of the 11 domains listed above.
	* These are currently not set up to be called through the Hugging Face API and must be extracted from the zip directly.
	---
	## Additional Information
	### Citation
	```
	@inproceedings{Ammanabrolu2020AAAI,
	title={Story Realization: Expanding Plot Events into Sentences},
	author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl},
	journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
	year={2020},
	volume={34},
	number={05},
	url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
	}
	```
	---
	### Licensing
	The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/