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README.md

@@ -20,6 +20,8 @@ configs:
   data_files:
   - split: corpus
     path: data/corpus.jsonl
+  - split: corpus_summary
+    path: data/corpus_summary.jsonl
 - config_name: pairs
   data_files:
   - split: train
@@ -36,6 +38,8 @@ dataset_info:
   splits:
   - name: corpus
     num_examples: 109650
+  - name: corpus_summary
+    num_examples: 21119
 - config_name: pairs
   features:
   - name: query
@@ -70,12 +74,13 @@ Hugging Face `google-research-datasets/natural_questions` snapshot.
 | Split            | Rows         |
 |------------------|--------------|
 | `corpus`         | 109,650 |
+| `corpus_summary`         | 21,119 |
 | `train` pairs    | 307,373  |
 | `validation` pairs | 7,830 |
 
 * Train pairs with non-empty `long_answer`:  **152,148 / 307,373** (49.5%)
 * Train pairs with non-empty `short_answer`: **106,926 / 307,373** (34.8%)
-* Date built: 2026-04-30
+* Date built: 2026-05-06
 
 ## Schema
 
@@ -171,6 +176,9 @@ ds = load_dataset("<your-username>/NQ320K-NCI", "pairs")
 print(ds["train"].features["nq_id"])  # Value(dtype='string', id=None)
 ```
 
+## Corpus Summary
+We additionally present a subset of corpus as a summarized text. We use `sshleifer/distilbart-cnn-12-6` model for the summarization task.
+
 ## License & attribution
 
 This dataset is a derivative of the Natural Questions dataset by Google