Update README.md

README.md

@@ -1,84 +1,133 @@
----
-license: cc-by-nc-4.0
-dataset_info:
-  features:
-  - name: summary
-    dtype: string
-  - name: cluster_id
-    dtype: string
-  - name: summary_id
-    dtype: string
-  - name: article_id
-    dtype: string
-  - name: summary_title
-    dtype: string
-  - name: article_title
-    dtype: string
-  - name: summary_domain
-    dtype: string
-  - name: article_domain
-    dtype: string
-  - name: summary_maintext
-    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
-  - name: date_publish
-    dtype: timestamp[us]
-  - name: url
-    dtype: string
-  - name: abstractiveness_bin
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 4181641242
-    num_examples: 1349911
-  - name: validation
-    num_bytes: 32042449
-    num_examples: 10000
-  - name: test
-    num_bytes: 32577217
-    num_examples: 10000
-  download_size: 1479040048
-  dataset_size: 4246260908
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
----
+---
+license: cc-by-nc-4.0
+dataset_info:
+  features:
+  - name: summary
+    dtype: string
+  - name: cluster_id
+    dtype: string
+  - name: summary_id
+    dtype: string
+  - name: article_id
+    dtype: string
+  - name: summary_title
+    dtype: string
+  - name: article_title
+    dtype: string
+  - name: summary_domain
+    dtype: string
+  - name: article_domain
+    dtype: string
+  - name: summary_maintext
+    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
+  - name: date_publish
+    dtype: timestamp[us]
+  - name: url
+    dtype: string
+  - name: abstractiveness_bin
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4181641242
+    num_examples: 1349911
+  - name: validation
+    num_bytes: 32042449
+    num_examples: 10000
+  - name: test
+    num_bytes: 32577217
+    num_examples: 10000
+  download_size: 1479040048
+  dataset_size: 4246260908
+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 summary, article url and meta-data of the CCSum dataset. The articles can be downloaded from CC-News.
+
+## 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
+```python
+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.