README.md

metadata

license: cc-by-nc-4.0
dataset_info:
  features:
    - name: summary
      dtype: string
    - name: url
      dtype: string
    - name: date_publish
      dtype: timestamp[us]
    - name: article_title
      dtype: string
    - name: id
      dtype: string
    - name: article_domain
      dtype: string
    - name: abstractiveness_bin
      dtype: string
    - name: cluster_id
      dtype: string
    - name: summary_id
      dtype: string
    - name: article_id
      dtype: string
    - name: summary_domain
      dtype: string
    - name: summary_word_count
      dtype: int64
    - name: summary_entity_count
      dtype: int64
    - name: entity_precision_constraint
      dtype: float64
    - name: entity_precision
      dtype: float64
    - name: simhash_distance
      dtype: int64
    - name: quotation_precision
      dtype: float64
    - name: title-title-similarity
      dtype: float32
    - name: summary-title-similarity
      dtype: float32
    - name: BERTScore-P (bert-large-uncased)
      dtype: float32
    - name: BERTScore-R (bert-large-uncased)
      dtype: float32
    - name: BERTScore-F1 (bert-large-uncased)
      dtype: float32
    - name: BERTScore-P (facebook/bart-large)
      dtype: float32
    - name: BERTScore-R (facebook/bart-large)
      dtype: float32
    - name: BERTScore-F1 (facebook/bart-large)
      dtype: float32
    - name: mint
      dtype: float64
    - name: lcsr
      dtype: float64
  splits:
    - name: train
      num_bytes: 1050376245
      num_examples: 1349911
    - name: validation
      num_bytes: 7785024
      num_examples: 10000
    - name: test
      num_bytes: 7798236
      num_examples: 10000
  download_size: 521533439
  dataset_size: 1065959505
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for CCSum [summary-only]

We release the meta data containing article url, title, summary (median length: 30 words), published date, id derived from sha2(maintext, 256), and other meta data associated with the CCSum dataset. Please download the articles based on the urls, and reach out to us if you encounter any issue with using the dataset.

Dataset Summary

CCSum is a large-scale and high-quality dataset for abstractive news summarization. It contains 1.3 million pairs of articles and reference summaries derived from 35 million news articles from CommonCrawl News. In creating this dataset, we cluster CommonCrawl News articles into news events from which we generate candidate article-summary pairs and apply strict filtering and a Bayesian optimization method that eliminates 99% of the candidate summaries. The human evaluation shows the proposed dataset has higher quality-in terms of factual consistency, informativeness, and coherence-than established abstractive summarization datasets.

Load dataset

from datasets import load_dataset
# Load the full dataset (both abstractive and extractive)
dataset = load_dataset("ccsum/CCSum")

# abstractive subset of the dataset
dataset_abstractive = dataset.filter(lambda x: x["abstractiveness_bin"] == "high")

# extractive subset of the dataset
dataset_extractive = dataset.filter(lambda x: x["abstractiveness_bin"] == "low")

Language

CCSum currently only supports English.

Main Data Fields

• id: a string that corresponds to the sha256 hash of the article and summary
• article: a string containing the body of the news article from CCNews
• summary: a string containing a summary for the article
• abstractiveness_bin: a string indicating if the abstractiveness level of the summary. high denotes the abstractive subset and low denotes the extractive subset.

Data Splits

The CNN/DailyMail dataset has 3 splits: train, validation, and test. Below are the statistics for Version 3.0.0 of the dataset.

Split	Total	Date range	Extractive	Abstractive
Train	1,349,911	1/2018 - 12/2021	674,939	674,972
Val.	10,000	1/2022 - 5/2022	4,853	5,147
Test	10,000	6/2022 - 12/2022	5,053	4,947

Dataset Creation

The dataset is created from CommonCrawl News. Please refer to our paper for more details: "CCSum: A Large-Scale and High-Quality Dataset for Abstractive News Summarization (NAACL 2024)."

Licensing Information

The CCSum dataset released under the cc-by-nc-4.0 license.