
# Dataset Card for LFQA Summary

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Repository:** [Repo](https://github.com/utcsnlp/lfqa_summary)
- **Paper:** [Concise Answers to Complex Questions: Summarization of Long-Form Answers](TODO)
- **Point of Contact:** acpotluri[at]utexas.edu

### Dataset Summary

This dataset contains summarization data for long-form question answers.

### Languages

The dataset contains data in English.

## Dataset Structure

### Data Instances

Each instance is a (question, long-form answer) pair from one of the three data sources -- ELI5, WebGPT, and NQ.

### Data Fields

Each instance is in a json dictionary format with the following fields:
* `type`: The type of the annotation, all data should have `summary` as the value.
* `dataset`: The dataset this QA pair belongs to, one of [`NQ`, `ELI5`, `Web-GPT`].
* `q_id`: The question id, same as the original NQ or ELI5 dataset.
* `a_id`: The answer id, same as the original ELI5 dataset. For NQ, we populate a dummy `a_id` (1).
* `question`: The question.
* `answer_paragraph`: The answer paragraph.
* `answer_sentences`: The list of answer sentences, tokenzied from the answer paragraph.
* `summary_sentences`: The list of summary sentence index (starting from 1).
* `is_summary_count`: The list of count of annotators selecting this sentence as summary for the sentence in `answer_sentences`.
* `is_summary_1`: List of boolean value indicating whether annotator one selected the corresponding sentence as a summary sentence.
* `is_summary_2`: List of boolean value indicating whether annotator two selected the corresponding sentence as a summary sentence.
* `is_summary_3`: List of boolean value indicating whether annotator three selected the corresponding sentence as a summary sentence.


### Data Splits

The train/dev/test are provided in the uploaded dataset.

## Dataset Creation

Please refer to our [paper](TODO) and datasheet for details on dataset creation, annotation process, and discussion of limitations.

## Additional Information

### Licensing Information

https://creativecommons.org/licenses/by-sa/4.0/legalcode

### Citation Information
```
@inproceedings{TODO,
  title     = {Concise Answers to Complex Questions: Summarization of Long-Form Answers},
  author    = {Potluri,Abhilash and Xu, Fangyuan and Choi, Eunsol},
  year      = 2023,
  booktitle = {Proceedings of the Annual Meeting of the Association for Computational Linguistics},
  note      = {Long paper}
}
```
task_categories:summarization size_categories:1K<n<10K language:en license:cc-by-sa-4.0 region:us
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"], "pretty_name": "lfqa_summary"}
TAGS
#task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc-by-sa-4.0 #region-us 


# Dataset Card for LFQA Summary

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
- Additional Information
  - Licensing Information
  - Citation Information

## Dataset Description

- Repository: Repo
- Paper: Concise Answers to Complex Questions: Summarization of Long-Form Answers
- Point of Contact: acpotluri[at]URL

### Dataset Summary

This dataset contains summarization data for long-form question answers.

### Languages

The dataset contains data in English.

## Dataset Structure

### Data Instances

Each instance is a (question, long-form answer) pair from one of the three data sources -- ELI5, WebGPT, and NQ.

### Data Fields

Each instance is in a json dictionary format with the following fields:
* 'type': The type of the annotation, all data should have 'summary' as the value.
* 'dataset': The dataset this QA pair belongs to, one of ['NQ', 'ELI5', 'Web-GPT'].
* 'q_id': The question id, same as the original NQ or ELI5 dataset.
* 'a_id': The answer id, same as the original ELI5 dataset. For NQ, we populate a dummy 'a_id' (1).
* 'question': The question.
* 'answer_paragraph': The answer paragraph.
* 'answer_sentences': The list of answer sentences, tokenzied from the answer paragraph.
* 'summary_sentences': The list of summary sentence index (starting from 1).
* 'is_summary_count': The list of count of annotators selecting this sentence as summary for the sentence in 'answer_sentences'.
* 'is_summary_1': List of boolean value indicating whether annotator one selected the corresponding sentence as a summary sentence.
* 'is_summary_2': List of boolean value indicating whether annotator two selected the corresponding sentence as a summary sentence.
* 'is_summary_3': List of boolean value indicating whether annotator three selected the corresponding sentence as a summary sentence.


### Data Splits

The train/dev/test are provided in the uploaded dataset.

## Dataset Creation

Please refer to our paper and datasheet for details on dataset creation, annotation process, and discussion of limitations.

## Additional Information

### Licensing Information

URL



# Dataset Card for LFQA Summary ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
- Additional Information
  - Licensing Information
  - Citation Information ## Dataset Description

- Repository: Repo
- Paper: Concise Answers to Complex Questions: Summarization of Long-Form Answers
- Point of Contact: acpotluri[at]URL ### Dataset Summary

This dataset contains summarization data for long-form question answers. ### Languages

The dataset contains data in English. ## Dataset Structure ### Data Instances

Each instance is a (question, long-form answer) pair from one of the three data sources -- ELI5, WebGPT, and NQ. ### Data Fields

Each instance is in a json dictionary format with the following fields:
* 'type': The type of the annotation, all data should have 'summary' as the value.
* 'dataset': The dataset this QA pair belongs to, one of ['NQ', 'ELI5', 'Web-GPT'].
* 'q_id': The question id, same as the original NQ or ELI5 dataset.
* 'a_id': The answer id, same as the original ELI5 dataset. For NQ, we populate a dummy 'a_id' (1).
* 'question': The question.
* 'answer_paragraph': The answer paragraph.
* 'answer_sentences': The list of answer sentences, tokenzied from the answer paragraph.
* 'summary_sentences': The list of summary sentence index (starting from 1).
* 'is_summary_count': The list of count of annotators selecting this sentence as summary for the sentence in 'answer_sentences'.
* 'is_summary_1': List of boolean value indicating whether annotator one selected the corresponding sentence as a summary sentence.
* 'is_summary_2': List of boolean value indicating whether annotator two selected the corresponding sentence as a summary sentence.
* 'is_summary_3': List of boolean value indicating whether annotator three selected the corresponding sentence as a summary sentence. ### Data Splits

The train/dev/test are provided in the uploaded dataset. ## Dataset Creation

Please refer to our paper and datasheet for details on dataset creation, annotation process, and discussion of limitations. ## Additional Information ### Licensing Information

URL
TAGS
#task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc-by-sa-4.0 #region-us 
 # Dataset Card for LFQA Summary ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
- Additional Information
  - Licensing Information
  - Citation Information ## Dataset Description

- Repository: Repo
- Paper: Concise Answers to Complex Questions: Summarization of Long-Form Answers
- Point of Contact: acpotluri[at]URL ### Dataset Summary

This dataset contains summarization data for long-form question answers. ### Languages

The dataset contains data in English. ## Dataset Structure ### Data Instances

Each instance is a (question, long-form answer) pair from one of the three data sources -- ELI5, WebGPT, and NQ. ### Data Fields

Each instance is in a json dictionary format with the following fields:
* 'type': The type of the annotation, all data should have 'summary' as the value.
* 'dataset': The dataset this QA pair belongs to, one of ['NQ', 'ELI5', 'Web-GPT'].
* 'q_id': The question id, same as the original NQ or ELI5 dataset.
* 'a_id': The answer id, same as the original ELI5 dataset. For NQ, we populate a dummy 'a_id' (1).
* 'question': The question.
* 'answer_paragraph': The answer paragraph.
* 'answer_sentences': The list of answer sentences, tokenzied from the answer paragraph.
* 'summary_sentences': The list of summary sentence index (starting from 1).
* 'is_summary_count': The list of count of annotators selecting this sentence as summary for the sentence in 'answer_sentences'.
* 'is_summary_1': List of boolean value indicating whether annotator one selected the corresponding sentence as a summary sentence.
* 'is_summary_2': List of boolean value indicating whether annotator two selected the corresponding sentence as a summary sentence.
* 'is_summary_3': List of boolean value indicating whether annotator three selected the corresponding sentence as a summary sentence. ### Data Splits

The train/dev/test are provided in the uploaded dataset. ## Dataset Creation

Please refer to our paper and datasheet for details on dataset creation, annotation process, and discussion of limitations. ## Additional Information ### Licensing Information

URL