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--- |
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license: cc-by-nc-4.0 |
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dataset_info: |
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features: |
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- name: summary |
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dtype: string |
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- name: url |
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dtype: string |
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- name: date_publish |
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dtype: timestamp[us] |
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- name: article_title |
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dtype: string |
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- name: id |
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dtype: string |
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- name: article_domain |
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dtype: string |
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- name: abstractiveness_bin |
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dtype: string |
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- name: cluster_id |
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dtype: string |
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- name: summary_id |
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dtype: string |
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- name: article_id |
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dtype: string |
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- name: summary_domain |
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dtype: string |
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- name: summary_word_count |
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dtype: int64 |
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- name: summary_entity_count |
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dtype: int64 |
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- name: entity_precision_constraint |
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dtype: float64 |
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- name: entity_precision |
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dtype: float64 |
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- name: simhash_distance |
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dtype: int64 |
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- name: quotation_precision |
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dtype: float64 |
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- name: title-title-similarity |
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dtype: float32 |
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- name: summary-title-similarity |
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dtype: float32 |
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- name: BERTScore-P (bert-large-uncased) |
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dtype: float32 |
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- name: BERTScore-R (bert-large-uncased) |
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dtype: float32 |
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- name: BERTScore-F1 (bert-large-uncased) |
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dtype: float32 |
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- name: BERTScore-P (facebook/bart-large) |
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dtype: float32 |
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- name: BERTScore-R (facebook/bart-large) |
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dtype: float32 |
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- name: BERTScore-F1 (facebook/bart-large) |
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dtype: float32 |
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- name: mint |
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dtype: float64 |
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- name: lcsr |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 1050376245 |
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num_examples: 1349911 |
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- name: validation |
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num_bytes: 7785024 |
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num_examples: 10000 |
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- name: test |
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num_bytes: 7798236 |
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num_examples: 10000 |
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download_size: 521533439 |
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dataset_size: 1065959505 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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## Dataset Card for CCSum [summary-only] |
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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. |
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Please download the articles based on the urls, and reach out to us if you encounter any issue with using the dataset. |
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## Dataset Summary |
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CCSum is a large-scale and high-quality dataset for abstractive news summarization. |
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It contains 1.3 million pairs of articles and reference summaries derived from 35 million news articles from CommonCrawl News. |
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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. |
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The human evaluation shows the proposed dataset has higher quality-in terms of factual consistency, informativeness, and coherence-than established abstractive summarization datasets. |
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## Load dataset |
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```python |
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from datasets import load_dataset |
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# Load the full dataset (both abstractive and extractive) |
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dataset = load_dataset("ccsum/CCSum") |
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# abstractive subset of the dataset |
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dataset_abstractive = dataset.filter(lambda x: x["abstractiveness_bin"] == "high") |
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# extractive subset of the dataset |
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dataset_extractive = dataset.filter(lambda x: x["abstractiveness_bin"] == "low") |
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``` |
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## Language |
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CCSum currently only supports English. |
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## Main Data Fields |
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- `id`: a string that corresponds to the sha256 hash of the article and summary |
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- `article`: a string containing the body of the news article from CCNews |
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- `summary`: a string containing a summary for the article |
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- `abstractiveness_bin`: a string indicating if the abstractiveness level of the summary. `high` denotes the abstractive subset and `low` denotes the extractive subset. |
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### Data Splits |
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The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset. |
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| Split | Total | Date range | Extractive | Abstractive | |
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|-------|-----------|------------------|------------|------------| |
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| Train | 1,349,911 | 1/2018 - 12/2021 | 674,939 | 674,972 | |
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| Val. | 10,000 | 1/2022 - 5/2022 | 4,853 | 5,147 | |
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| Test | 10,000 | 6/2022 - 12/2022 | 5,053 | 4,947 | |
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## Dataset Creation |
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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)." |
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### Licensing Information |
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The CCSum dataset released under the cc-by-nc-4.0 license. |
