Update README.md

README.md

@@ -14,6 +14,9 @@ BART \[1\] fine-tuned for extractive summarization on a dataset of movie and boo
 
 Continued fine-tuning from the BART-large-cnn checkpoint, which was fine-tuned on the CNN Daily Mail, which is more extractive than abstractive.
 
+**Compare**: The smaller model [BART-base-quotes](https://huggingface.co/ChrisBridges/bart-base-quotes) achieved slightly smaller ROUGE scores,
+but favors shorter quotes (~1/4 length on average).
+
 ## Training Description
 
 ### Dataset
@@ -66,8 +69,8 @@ Additionally, we report the average predicted quote length, the number of epochs
 | BART-large-cnn | 0.4384 ± 0.0225 | 0.3693 ± 0.0197 | 0.4165 ± 0.0239 | 0.4317 ± 0.0234 | 81.8623 ± 1.5324 | 28.23  | 3:48:24 |
 
 ## References
-\[1\] (BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
-)[https://arxiv.org/abs/1910.13461]  
-\[2\] (You Had Me at Hello: How Phrasing Affects Memorability)[https://aclanthology.org/P12-1094/]  
-\[3\] (Quote Detection: A New Task and Dataset for NLP)[https://aclanthology.org/2023.latechclfl-1.3/]  
-\[4\] (Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer)[https://arxiv.org/abs/1910.10683]
+\[1\] [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
+](https://arxiv.org/abs/1910.13461)  
+\[2\] [You Had Me at Hello: How Phrasing Affects Memorability](https://aclanthology.org/P12-1094/)  
+\[3\] [Quote Detection: A New Task and Dataset for NLP](https://aclanthology.org/2023.latechclfl-1.3/)  
+\[4\] [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)