README.md

---
dataset_info:
  features:
  - name: 'Unnamed: 0'
    dtype: int64
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: abstract
    dtype: string
  - name: introduction
    dtype: string
  splits:
  - name: train
    num_bytes: 1844987
    num_examples: 421
  - name: validation
    num_bytes: 949747
    num_examples: 211
  - name: test
    num_bytes: 1403003
    num_examples: 320
  download_size: 2341682
  dataset_size: 4197737
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- summarization
- question-answering
language:
- en
tags:
- nlp-research-paper-abstract
- nlp-research-paper
- question-generation
pretty_name: NLP_Papers_to_Question_Generation
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
This dataset was created by modifying and adapting the [allenai/QASPER: a dataset for question answering on scientific research papers](https://huggingface.co/datasets/allenai/qasper) dataset 
and **aims to generate Question-Answer Pairs from the Abstract, Introduction of an NLP Paper**.



### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
- First, we extracted the abstract, introduction of each NLP paper from QASPER dataset.
- We also extracted only the rows labeled question and answer that had an abstract answer rather than extractive.  
- train : 421 rows
- validation : 211 rows
- test : 320 rows

- **Curated by:** [@UNIST-Eunchan](https://huggingface.co/UNIST-Eunchan)

- 
### Dataset Sources

This data is made by applying and processing [allenai/qasper](https://huggingface.co/datasets/allenai/qasper)

<!-- Provide the basic links for the dataset. -->

- **Repository:** [allenai/qasper](https://huggingface.co/datasets/allenai/qasper)


## Uses

- **Question Generation from Research Paper**
- **Long-Document Summarization** 
- **Question-based Summarization**
<!-- Address questions around how the dataset is intended to be used. -->


## Dataset Creation

### Curation Rationale

Long Document Summarization datasets, especially those for Research Paper Summarization, are very limited and scarce. 

We tweak the existing data to provide domains and QA pairs specific to NLP among Research Papers. 

We expect to be able to generate multiple QA pairs if we let the model sample through training. 

We will release the fine-tuned model in the future.