
# Inspec Benchmark Dataset for Keyphrase Generation

## About

Inspec is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 2,000 abstracts of scientific papers collected from the [Inspec database](https://www.theiet.org/resources/inspec/).
Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
Details about the inspec dataset can be found in the original paper [(Hulth, 2003)][hulth-2003].
 

Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].

Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`.

## Content and statistics

The dataset is divided into the following three splits:

| Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train      | 1,000       | 141.7  | 9.79         | 78.00     | 9.85        | 6.22    | 5.93     |
| Validation | 500         | 132.2  | 9.15         | 77.96     | 9.82        | 6.75    | 5.47     |
| Test       | 500         | 134.8  | 9.83         | 78.70     | 9.92        | 6.48    | 4.91     |

The following data fields are available :

- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.

## References

- (Hulth, 2003) Anette Hulth. 2003.
  [Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028). 
  In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 216-223.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
  [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). 
  In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.

[hulth-2003]: https://aclanthology.org/W03-1028/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
task_categories:text-generation annotations_creators:unknown language_creators:unknown multilinguality:monolingual size_categories:1K<n<10K language:en license:unknown region:us
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TAGS
#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #region-us 

Inspec Benchmark Dataset for Keyphrase Generation
=================================================


About
-----


Inspec is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 2,000 abstracts of scientific papers collected from the Inspec database.
Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
Details about the inspec dataset can be found in the original paper [(Hulth, 2003)](URL).


Reference (indexer-assigned) keyphrases are also categorized under the PRMU (Present-Reordered-Mixed-Unseen) scheme as proposed in [(Boudin and Gallina, 2021)](URL).


Text pre-processing (tokenization) is carried out using 'spacy' ('en\_core\_web\_sm' model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in 'nltk') is applied before reference keyphrases are matched against the source text.
Details about the process can be found in 'URL'.


Content and statistics
----------------------


The dataset is divided into the following three splits:



The following data fields are available :


* id: unique identifier of the document.
* title: title of the document.
* abstract: abstract of the document.
* keyphrases: list of reference keyphrases.
* prmu: list of Present-Reordered-Mixed-Unseen categories for reference keyphrases.


References
----------


* (Hulth, 2003) Anette Hulth. 2003.
Improved automatic keyword extraction given more linguistic knowledge.
In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 216-223.
* (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness.
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.



TAGS
#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #region-us 

passage: TAGS
#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #region-us 
