task_categories:summarization size_categories:100K<n<1M language:en license:apache-2.0 region:us
---
language:
- en
license: apache-2.0
size_categories:
- 100K<n<1M
task_categories:
- summarization
pretty_name: Long-Form Article Summarization Dataset
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: text
    dtype: string
  - name: summary
    dtype: string
  splits:
  - name: train
    num_bytes: 2243293725
    num_examples: 105256
  download_size: 880664627
  dataset_size: 2243293725
---
**Dataset Name:** Long-Form Article Summarization Dataset

**Description:**
The Long-Form Article Summarization Dataset is meticulously curated for the purpose of fine-tuning Natural Language Processing (NLP) models specifically tailored for summarization tasks. It is a rich collection of long-form articles that have been carefully condensed and summarized. The dataset provides a diverse range of topics and writing styles, making it an invaluable resource for researchers and practitioners working on summarization algorithms and applications.

**Data Sources:**
1. **Billsum:** This dataset includes summaries of U.S. congressional and state bills, providing insights into legislative documents.
2. **Scientific Papers:** A collection of scientific papers covering various disciplines, enabling a deep dive into research-oriented content.
3. **Multi_news:** This dataset incorporates news articles, offering a blend of current events and journalistic writing styles.
4. **CCDV/Pubmed-Summarization:** Focused on biomedical literature, this dataset contains summaries from Pubmed articles, offering specialized content related to the field of medicine and life sciences.

**Data Combination:**
The Long-Form Article Summarization Dataset is an amalgamation of the above-mentioned datasets. By combining these diverse sources, the dataset achieves a comprehensive coverage of topics, styles, and domains. This fusion enhances the dataset's versatility and applicability across a wide array of domains, making it a valuable asset for NLP research and development.

**Data Preprocessing:**
To ensure equal representation of unique domains and to manage the scale of the dataset, large datasets were down-sampled. This meticulous preprocessing step guarantees that each domain is adequately represented, promoting a balanced and unbiased training environment for NLP models.

**Intended Use:**
This dataset is specifically designed for fine-tuning NLP models focused on summarization tasks. Researchers and developers can utilize this dataset to train and evaluate their algorithms for generating concise and informative summaries from long-form articles. The dataset's diverse origins and careful preprocessing make it an ideal choice for enhancing the summarization capabilities of NLP models.

**Access:**
The Long-Form Article Summarization Dataset is available for research purposes and can be accessed through authorized channels. Researchers and developers interested in using this dataset are encouraged to adhere to ethical guidelines and data usage policies governing the respective sources.

**Citation:**
Researchers and practitioners are expected to cite the original sources of the datasets used in this amalgamation, namely "Billsum," "Scientific Papers," "Multi_news," and "CCDV/Pubmed-Summarization," in addition to acknowledging the creation of the Long-Form Article Summarization Dataset in their publications and research outputs.

This dataset card provides an overview of the Long-Form Article Summarization Dataset, outlining its sources, preprocessing methods, intended use, and access guidelines, ensuring transparent and responsible utilization of the valuable data it encapsulates.
