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Dataset Card for Science Fiction TV Show Plots Corpus

Dataset Description

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

Contains plot summaries from:

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.


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)

    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


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)}, 


The Creative Commons Attribution 4.0 International License.

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