# Dataset Card for "ILSUM-1.0"

### Dataset Summary

Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.

The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.

Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~
### Languages
- Hindi
- Gujarati
- English

### Data Fields
~~~
- id: Unique id of each datapoint
- Article: Entire News article
- Headline: Headline of News Article
- Summary: Summary of News Article
~~~

### Data Splits
Data for all three languages is divided into three splits train, validation and test.

### Load dataset using hf-dataset class
```python
from datasets import load_dataset

dataset = load_dataset("ILSUM/ILSUM-1.0", "Hindi")
# you can use any of the following config names as a second argument:
# "English", "Hindi", "Gujarati"

```

### Citation Information
If you are using the dataset or the models please cite the following paper
~~~
@article{satapara2022findings,
  title={Findings of the first shared task on indian language summarization (ilsum): Approaches, challenges and the path ahead},
  author={Satapara, Shrey and Modha, Bhavan and Modha, Sandip and Mehta, Parth},
  journal={Working Notes of FIRE},
  pages={9--13},
  year={2022}
}
~~~


### Contributions
- Bhavan Modha, University Of Texas at Dallas, USA
- Shrey Satapara, Indian Institute Of Technology, Hyderabad, India
- Sandip Modha, LDRP-ITR, Gandhinagar, India
- Parth Mehta, Parmonic, USA
<!--## Dataset Description

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- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 



### Supported Tasks and Leaderboards

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## Dataset Structure

### Data Instances

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[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

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## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

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### Other Known Limitations

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## Additional Information

### Dataset Curators

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### Licensing Information

[More Information Needed]


task_categories:summarization size_categories:1K<n<10K size_categories:10K<n<100K language:hi language:gu language:en region:us
{"language": ["hi", "gu", "en"], "size_categories": ["1K<n<10K", "10K<n<100K"], "task_categories": ["summarization"], "config_names": ["English", "Hindi", "Gujarati"], "configs": [{"config_name": "Hindi", "data_files": [{"split": "train", "path": "Hindi/train.csv"}, {"split": "test", "path": "Hindi/test.csv"}, {"split": "validation", "path": "Hindi/val.csv"}], "default": true}, {"config_name": "Gujarati", "data_files": [{"split": "train", "path": "Gujarati/train.csv"}, {"split": "test", "path": "Gujarati/test.csv"}, {"split": "validation", "path": "Gujarati/val.csv"}]}, {"config_name": "English", "data_files": [{"split": "train", "path": "English/train.csv"}, {"split": "test", "path": "English/test.csv"}, {"split": "validation", "path": "English/val.csv"}]}]}
TAGS
#task_categories-summarization #size_categories-1K<n<10K #size_categories-10K<n<100K #language-Hindi #language-Gujarati #language-English #region-us 

# Dataset Card for "ILSUM-1.0"

### Dataset Summary

Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.

The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.

Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~
### Languages
- Hindi
- Gujarati
- English

### Data Fields
~~~
- id: Unique id of each datapoint
- Article: Entire News article
- Headline: Headline of News Article
- Summary: Summary of News Article
~~~

### Data Splits
Data for all three languages is divided into three splits train, validation and test.

### Load dataset using hf-dataset class



If you are using the dataset or the models please cite the following paper
~~~
@article{satapara2022findings,
  title={Findings of the first shared task on indian language summarization (ilsum): Approaches, challenges and the path ahead},
  author={Satapara, Shrey and Modha, Bhavan and Modha, Sandip and Mehta, Parth},
  journal={Working Notes of FIRE},
  pages={9--13},
  year={2022}
}
~~~


### Contributions
- Bhavan Modha, University Of Texas at Dallas, USA
- Shrey Satapara, Indian Institute Of Technology, Hyderabad, India
- Sandip Modha, LDRP-ITR, Gandhinagar, India
- Parth Mehta, Parmonic, USA
<!--## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 



### Supported Tasks and Leaderboards





## Dataset Structure

### Data Instances









### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information




# Dataset Card for "ILSUM-1.0" ### Dataset Summary

Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.

The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.

Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~ ### Languages
- Hindi
- Gujarati
- English ### Data Fields
~~~
- id: Unique id of each datapoint
- Article: Entire News article
- Headline: Headline of News Article
- Summary: Summary of News Article
~~~ ### Data Splits
Data for all three languages is divided into three splits train, validation and test. ### Load dataset using hf-dataset class



If you are using the dataset or the models please cite the following paper
~~~
@article{satapara2022findings,
  title={Findings of the first shared task on indian language summarization (ilsum): Approaches, challenges and the path ahead},
  author={Satapara, Shrey and Modha, Bhavan and Modha, Sandip and Mehta, Parth},
  journal={Working Notes of FIRE},
  pages={9--13},
  year={2022}
}
~~~ ### Contributions
- Bhavan Modha, University Of Texas at Dallas, USA
- Shrey Satapara, Indian Institute Of Technology, Hyderabad, India
- Sandip Modha, LDRP-ITR, Gandhinagar, India
- Parth Mehta, Parmonic, USA
<!--## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Supported Tasks and Leaderboards ## Dataset Structure ### Data Instances ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information
TAGS
#task_categories-summarization #size_categories-1K<n<10K #size_categories-10K<n<100K #language-Hindi #language-Gujarati #language-English #region-us 
 # Dataset Card for "ILSUM-1.0" ### Dataset Summary

Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.

The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.

Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~ ### Languages
- Hindi
- Gujarati
- English ### Data Fields
~~~
- id: Unique id of each datapoint
- Article: Entire News article
- Headline: Headline of News Article
- Summary: Summary of News Article
~~~ ### Data Splits
Data for all three languages is divided into three splits train, validation and test. ### Load dataset using hf-dataset class



If you are using the dataset or the models please cite the following paper
~~~
@article{satapara2022findings,
  title={Findings of the first shared task on indian language summarization (ilsum): Approaches, challenges and the path ahead},
  author={Satapara, Shrey and Modha, Bhavan and Modha, Sandip and Mehta, Parth},
  journal={Working Notes of FIRE},
  pages={9--13},
  year={2022}
}
~~~ ### Contributions
- Bhavan Modha, University Of Texas at Dallas, USA
- Shrey Satapara, Indian Institute Of Technology, Hyderabad, India
- Sandip Modha, LDRP-ITR, Gandhinagar, India
- Parth Mehta, Parmonic, USA
<!--## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Supported Tasks and Leaderboards ## Dataset Structure ### Data Instances ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information
passage: TAGS
#task_categories-summarization #size_categories-1K<n<10K #size_categories-10K<n<100K #language-Hindi #language-Gujarati #language-English #region-us 
# Dataset Card for "ILSUM-1.0"### Dataset Summary

Automatic text summarization for Indian languages has received surprisingly little attention from the NLP research community. While large scale datasets exist for a number of languages like English, Chinese, French, German, Spanish, etc. no such datasets exist for any Indian languages. Most existing datasets are either not public, or are too small to be useful. Through this shared task we aim to bridge the existing gap by creating reusable corpora for Indian Language Summarization. In the first edition we cover two major indian languages Hindi and Gujarati, which have over 350 million and over 50 million speakers respectively. Apart from this we also include Indian English, a widely regonized dialect which can be substantially different from English spoken elsewhere.

The dataset for this task is built using articles and headline pairs from several leading newspapers of the country. We provide ~10,000 news articles for each language. The task is to generate a meaningful fixed length summary, either extractive or abstractive, for each article. While several previous works in other languages use news artciles - headlines pair, the current dataset poses a unique challenge of code-mixing and script mixing. It is very common for news articles to borrow phrases from english, even if the article itself is written in an Indian Language.

Examples like these are a common occurence both in the headlines as well as in the articles.
~~~
- "IND vs SA, 5મી T20 તસવીરોમાં: વરસાદે વિલન બની મજા બગાડી" (India vs SA, 5th T20 in pictures: rain spoils the match)
- "LIC के IPO में पैसा लगाने वालों का टूटा दिल, आई एक और नुकसानदेह खबर" (Investors of LIC IPO left broken hearted, yet another bad news).
~~~### Languages
- Hindi
- Gujarati
- English