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kpcrowd

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Dataset Summary

A dataset for benchmarking keyphrase extraction and generation techniques from english news articles. For more details about the dataset please refer the original paper - https://arxiv.org/abs/1306.4886

Original source of the data -

Dataset Structure

Dataset Statistics

Table 1: Statistics on the length of the extractive keyphrases for Train, Test splits of kpcrowd dataset.

Train Test
Single word 81.62% 80.27%
Two words 14.41% 15.44%
Three words 2.79% 3.36%
Four words 0.78% 0.56%
Five words 0.20% 0.25%
Six words 0.12% 0.05%
Seven words 0% 0.05%
Eight words 0.01% 0%

Table 2: Statistics on the length of the abstractive keyphrases for Train, Test splits of kpcrowd dataset.

Train Test
Zero words 0.24% 0%
Single word 22.38 % 21.81%
Two words 45.14% 43.03%
Three words 18.35% 19.69%
Four words 7.71% 7.28%
Five words 3.09% 3.94%
Six words 1.51% 3.33%
Seven words 0.82% 0.61%%
Eight words 0.55% 0.30%
Nine words 0.17% 0%

Table 3: General statistics of the kpcrowd dataset.

Type of Analysis Train Test
Annotator Type Authors Authors
Document Type News Articles News Articles
No. of Documents 450 50
Avg. Document length (words) 511.89 465.3
Max Document length (words) 7006 1609
Max no. of abstractive keyphrases in a document 66 30
Min no. of abstractive keyphrases in a document 0 0
Avg. no. of abstractive keyphrases per document 6.45 6.6
Max no. of extractive keyphrases in a document 231 86
Min no. of extractive keyphrases in a document 5 9
Avg. no. of extractive keyphrases per document 42.81 39.24

Data Fields

  • id: unique identifier of the document.
  • document: Whitespace separated list of words in the document.
  • doc_bio_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
  • extractive_keyphrases: List of all the present keyphrases.
  • abstractive_keyphrase: List of all the absent keyphrases.

Data Splits

Split #datapoints
Train 450
Test 50

Usage

Full Dataset 

from datasets import load_dataset

# get entire dataset
dataset = load_dataset("midas/kpcrowd", "raw")

# sample from the train split
print("Sample from train dataset split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")

# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")

Output

Sample from training data split
Fields in the sample:  ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document:  ['James', 'Cameron', 'and', 'the', 'Future', 'of', 'Cinema', 'This', 'past', 'week', 'at', 'Cinemacon', ',', 'which', 'is', 'known', 'as', 'the', "''", '``', 'official', 'convention', 'of', 'the', 'National', 'Organization', 'of', 'Theatre', 'Owners', "''", "''", 'or', 'NATO', '-LRB-', 'really', '?', '-RRB-', ',', 'industry', 'professionals', 'of', 'all', 'sorts', 'gathered', 'at', 'Caesar', "'s", 'Palace', 'in', 'Las', 'Vegas', '.', 'The', 'convention', ',', 'previously', 'known', 'as', 'ShoWest', 'is', "''", '``', 'the', 'largest', 'cinema', 'trade', 'show', 'in', 'the', 'world', "''", "''", '-LRB-', 'www.cinemacon.com', '-RRB-', 'It', 'was', 'at', 'this', 'convention', 'that', 'filmmaker', 'James', 'Cameron', '-LRB-', 'Titanic', ',', 'Avatar', '-RRB-', 'delivered', 'a', 'presentation', 'entitled', "''", '``', 'A', 'Demonstration', 'and', 'Exclusive', 'Look', 'at', 'the', 'Future', 'of', 'Cinema', '.', "''", "''", 'The', 'last', 'time', 'Cameron', 'spoke', 'at', 'ShoWest', ',', 'he', 'and', 'George', 'Lucas', 'had', 'presented', 'a', 'plea', 'to', 'the', 'movie', 'industry', 'to', 'begin', 'its', 'huge', 'investment', 'in', 'digital', 'filmmaking', 'technology', 'in', 'preparation', 'of', 'the', '3D', 'revolution', 'that', 'was', 'bound', 'to', 'take', 'over', 'cinema', '.', 'One', 'year', 'removed', 'from', 'the', 'release', 'of', 'Cameron', "'s", 'technologically', 'groundbreaking', 'and', 'box', 'office', 'titan', 'Avatar', ',', 'the', 'film', 'industry', 'seems', 'to', 'have', 'done', 'exactly', 'what', 'Cameron', 'and', 'Lucas', 'predicted', '.', 'With', 'the', 'addition', 'of', 'digital', 'projection', 'systems', 'to', 'nearly', 'every', 'major', 'cineplex', 'or', 'theater', 'around', 'the', 'nation', 'and', 'of', 'course', 'the', 'overwhelming', 'use', 'of', '3D', ',', 'one', 'can', 'not', 'help', 'but', 'trust', 'that', 'Cameron', 'knows', 'what', 'he', 'is', 'talking', 'about.When', 'he', 'spoke', 'this', 'year', 'at', 'Cinemacon', ',', 'he', ',', 'once', 'again', ',', 'spoke', 'of', 'a', 'revolution', '.', 'Instead', 'of', 'promoting', '3D', 'cinema', ',', 'this', 'time', 'around', 'Cameron', 'talked', 'framerates', '.', 'Framerates', ',', 'for', 'those', 'not', 'fluent', 'in', 'film', 'jargon', ',', 'is', 'the', 'term', 'used', 'to', 'describe', 'the', 'speed', 'at', 'which', 'a', 'camera', 'shoots', 'and', 'subsequently', 'plays', 'back', 'individual', 'frames', 'on', 'a', 'film', 'strip', '.', 'The', 'industry', 'standard', 'has', 'been', '24', 'frames', 'per', 'second', '-LRB-', 'fps', '-RRB-', 'since', 'around', 'the', 'mid-20', "''", '``', 's', ',', 'as', 'it', 'is', 'believed', 'to', 'be', 'the', 'closest', 'to', 'mimicking', 'reality', '.', 'However', ',', 'filmmakers', 'have', 'always', 'experimented', 'with', 'framerates', 'whether', 'it', 'be', 'shooting', 'at', 'slower', 'frame', 'rates', 'to', 'produce', 'a', 'sensation', 'of', 'fast', 'motion', '-LRB-', 'think', ':', 'the', 'this', 'scene', 'in', 'Stanley', 'Kubrick', "'s", 'A', 'Clockwork', 'Orange', '-RRB-', 'or', 'shooting', 'at', 'faster', 'framerates', 'like', '48', 'fps', 'to', 'produce', 'what', 'is', 'known', 'as', 'slow', 'motion', '-LRB-', 'think', ':', 'sports', 'instant', 'replays', ',', 'or', 'this', 'funny', 'video', '.', "''", "''", 'Advertisement', 'Cameron', 'wants', 'the', 'industry', 'standard', 'to', 'change', '.', 'He', 'believes', 'that', 'by', 'making', 'the', 'industry', 'standard', 'something', 'like', '48', 'fps', ',', 'not', 'only', 'does', 'the', 'clarity', 'of', 'the', 'image', 'go', 'from', "''", '``', 'Good', "''", "''", 'to', "''", '``', 'Holy', 'S%@#!,', "''", "''", 'he', 'believes', 'it', 'will', 'improve', 'and', 'smooth', 'out', 'any', 'movement', 'that', 'the', 'camera', 'utilizes', '.', 'With', 'handheld', 'footage', 'practically', 'being', 'an', 'independent', 'film', 'standard', ',', 'it', 'will', 'help', 'translate', 'to', 'a', 'smoother', ',', 'more', 'pleasurable', 'film', 'experience', '.', 'His', 'argument', 'is', 'an', 'interesting', 'one', 'and', 'one', 'that', 'is', 'technically', 'relevant', 'and', 'affordable', 'for', 'all', 'kinds', 'of', 'filmmakers', '.', 'With', 'the', 'almost', 'overwhelming', 'transition', 'from', 'film', 'to', 'digital', ',', 'the', 'cost', 'of', 'shooting', 'at', 'higher', 'framerates', 'is', 'almost', 'null', 'and', 'void', '.', 'Most', 'of', 'the', 'newest', 'digital', 'video', 'cameras', 'like', 'Canon', "'s", '5D', 'and', '7D', 'already', 'shoot', 'at', 'a', 'standard', 'close', 'to', '30', 'fps', '.', 'So', ',', 'shooting', 'digitally', ',', 'one', 'does', "n't", 'have', 'to', 'empty', 'their', 'wallet', 'too', 'much', 'to', 'afford', 'to', 'shoot', 'at', 'higher', 'framerates', '.', 'That', 'being', 'said', ',', 'Cameron', "'s", 'proposal', 'presents', 'an', 'interesting', 'direction', 'for', 'the', 'future', 'of', 'cinema', '.', 'Many', 'filmmakers', 'like', 'Peter', 'Jackson', 'and', 'of', 'course', 'James', 'Cameron', 'have', 'already', 'experimented', 'with', 'increased', 'framerates', ',', 'and', 'their', 'arument', 'is', 'surely', 'a', 'compelling', 'one', ',', 'one', 'the', 'industry', 'will', 'have', 'to', 'keep', 'an', 'eye', 'on', '.']
Document BIO Tags:  ['B', 'I', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'I', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'B', 'I', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'B', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'B', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases:  ['technically relevant', 'framerates', 'interesting', 'las vegas', 'shooting', '7d', 'cinemacon', 'exclusive look', 'nato', 'clockwork', 'future of cinema', 'showest', 'george lucas', 'cinema', 'newest digital video cameras', 'peter jackson', 'holy s', 'advertisement', '30 fps', '48 fps', 'wwwcinemaconcom', 'national organization of theatre owners', 'james cameron', 'filmmakers', 'higher', 'movement', 'digital', 'jargon', 'independent', 'afford', 'keep', 'arument', 'wallet', 'subsequently', 'closest', 'motion', 'nation', 'sensation', 'pleasurable', 'experience', 'fluent', 'camera', 'cameron', 'clarity', 'revolution', 'industry standard', 'industry', 'preparation', 'scene', 'smoother', 'demonstration', 'huge investment', 'proposal', 'translate', 'produce', 'technology', 'footage', 'technologically', 'argument', 'affordable', 'box office', 'improve', 'standard']
Abstractive/absent Keyphrases:  ['increased framerates', "canon's 5d", "stanley kubrick's", 'future', 'titanic avatar', "caesar's palace", 'mid20s', "cameron's", 'exclusive', 'theatre owners', 'largest cinema', 'faster framerates', 'interesting direction', 'like peter jackson', 'newest', 'fast motion', 'official convention of the national organization', 'relevant', 'digital projection', 'movie industry']

-----------

Sample from test data split
Fields in the sample:  ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document:  ['&', 'lsquo', ';', 'Miral', '&', 'rsquo', ';', ':', 'Director', 'has', 'conflict', 'of', 'interest', '``', 'Miral', "''", 'Rated', 'PG', '-', '13', '.', 'At', 'Kendall', 'Square', 'Cinema', ':', 'C', '+', 'Painter-turned-director', 'Julian', 'Schnabel', '-LRB-', 'Oscar-nominated', 'for', 'his', 'exquisite', '``', 'The', 'Diving', 'Bell', 'and', 'the', 'Butterfly', "''", '-RRB-', 'has', 'built', 'a', 'terrific', 'second', 'career', 'from', 'filmed', 'biographies', '-LRB-', 'also', 'in-cluding', '``', 'Before', 'Night', 'Falls', "''", 'and', '``', 'Basquiat', "''", '-RRB-', 'that', 'deal', 'with', 'people', 'confined', 'by', 'circumstance', 'yearning', 'to', 'break', 'free', '.', 'I', "'d", 'love', 'to', 'report', 'that', 'his', 'fourth', 'film', ',', '``', 'Miral', ',', "''", 'continues', 'the', 'upward', 'trend', ',', 'but', 'the', 'screenplay', 'by', 'Schnabel', "'s", 'girlfriend', ',', 'Palestinian', 'journalist', 'Rula', 'Jebreal', '-LRB-', 'based', 'on', 'her', 'semiautobiographical', 'novel', '-RRB-', ',', 'contains', 'too', 'many', 'earnest', 'platitudes', 'in', 'its', 'one-sided', 'look', 'at', 'four', 'women', "'s", 'intertwining', 'lives', 'during', 'the', 'first', 'intifada', 'of', 'the', '1980s', '.', '-LRB-', 'Some', 'musical', 'choices', ',', 'such', 'as', 'Tom', 'Waits', "'", '``', 'All', 'the', 'World', 'Is', 'Green', "''", 'playing', 'over', 'a', 'climactic', 'funeral', ',', 'also', 'stand', 'out', 'in', 'a', 'bad', 'way', '.', '-RRB-', 'Miral', '-LRB-', 'Freida', 'Pinto', ',', '``', 'Slumdog', 'Millionaire', "''", '-RRB-', ',', 'the', 'young', 'Arab', 'woman', 'growing', 'up', 'in', 'Jerusalem', 'during', 'this', 'period', ',', 'does', "n't", 'enter', 'the', 'picture', 'immediately', ',', 'and', 'when', 'she', 'does', ',', 'she', 'does', "n't", 'have', 'much', 'to', 'say', '--', 'at', 'first', '.', '-LRB-', 'A', 'bit', 'of', 'a', 'good', 'thing', ',', 'because', 'Pinto', "'s", 'Indian-accented', 'English', 'does', "n't", 'quite', 'jibe', 'with', 'the', 'Arabic-tinged', 'tongues', 'of', 'her', 'co-stars', '.', '-RRB-', 'Beginning', 'in', 'war-torn', 'Jerusalem', 'circa', '1948', ',', 'when', '``', 'Mama', "''", 'Hind', 'Husseini', '-LRB-', 'Hiam', 'Abbass', ',', '``', 'The', 'Visitor', "''", '-RRB-', 'established', 'an', 'orphanage', 'for', 'refugees', 'that', 'quickly', 'becomes', 'home', 'to', '2,000', ',', 'the', 'movie', 'spans', 'the', 'next', '50', 'years', ',', 'and', 'though', 'Schnabel', "'s", 'artist', "'s", 'eye', 'is', 'on', 'display', ',', 'the', 'Israel', '/', 'Palestine', 'conflict', 'is', 'a', 'subject', 'that', 'he', 'never', 'brings', 'into', 'clear', 'focus', '--', 'at', 'least', 'with', 'regard', 'to', 'Israelis', '.', 'And', 'when', 'he', 'presents', 'what', 'some', 'would', 'believe', 'terrorist', 'actions', 'of', 'his', 'protagonists', ',', 'he', 'sidesteps', 'the', 'potentially', 'horrible', 'consequences', ':', 'a', 'disgraced', 'former', 'nurse', '--', 'a', 'lifesaver', '--', 'plants', 'a', 'bomb', 'in', 'a', 'crowded', 'movie', 'theater', '-LRB-', 'playing', ',', 'without', 'a', 'hint', 'of', 'subtlety', ',', 'Roman', 'Polanski', "'s", '``', 'Repulsion', "''", '-RRB-', 'but', 'the', 'device', 'fails', 'to', 'explode', ';', 'a', 'car', 'bomb', 'is', 'set', 'off', 'by', 'Miral', "'s", 'political', 'activist', 'boyfriend', '--', 'though', 'there', 'are', 'seemingly', 'no', 'casualties', '.', 'So', 'much', 'for', 'the', 'horrors', 'of', 'war', '.', '-LRB-', '``', 'Miral', "''", 'contains', 'anger-inducing', 'violent', 'themes', ',', 'particularly', 'for', 'those', 'sympathetic', 'to', 'Israel', '.', '-RRB-']
Document BIO Tags:  ['O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'I', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases:  ['conflict of interest', 'arabictinged tongues', 'slumdog millionaire', 'kendall square cinema', 'before night falls', 'earnest platitudes', 'basquiat', 'biographies', 'jerusalem circa', 'butterfly', 'musical choices', 'terrific second', 'tom waits', 'miral', 'director', 'conflict']
Abstractive/absent Keyphrases:  ['lsquomiralrsquo director', 'mama hind husseini', 'miral rated pg 13', 'painterturneddirector julian schnabel oscarnominated', 'schnabels girlfriend palestinian journalist rula jebreal', 'exquisite the diving bell', 'interest']

-----------

Keyphrase Extraction 

from datasets import load_dataset

# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/kpcrowd", "extraction")

print("Samples for Keyphrase Extraction")

# sample from the train split
print("Sample from train data split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")

# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")

Keyphrase Generation 

# get the dataset only for keyphrase generation
dataset = load_dataset("midas/kpcrowd", "generation")

print("Samples for Keyphrase Generation")

# sample from the train split
print("Sample from train data split")
test_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")

# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")

Citation Information 

@misc{marujo2013supervised,
      title={Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization}, 
      author={Luis Marujo and Anatole Gershman and Jaime Carbonell and Robert Frederking and João P. Neto},
      year={2013},
      eprint={1306.4886},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset