task_categories:summarization language:en license:cc-by-sa-4.0 size_categories:1K<n<10K region:us
---
license: cc-by-sa-4.0
task_categories:
- summarization
language:
- en
size_categories:
- 1K<n<10K
pretty_name: lfqa_summary
---

# 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}
}
```