Update README.md

README.md

@@ -1,30 +1,41 @@
----
-language: en
-license: apache-2.0
----
-
-
-HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022). 
-
-The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in the original github repo.
-
-* Note: due to the difference between the implementations of the original Longformer and the Huggingface LED model, the results of converted models are slightly different. We run a sanity check on both fine-tuned and non fine-tuned models, and show the results below:
-
-| Model | Rouge-1 | Rouge-2 | Rouge-L |
-| --- | ----------- |----------- |----------- |
-| PRIMERA | 42.0 | 13.6 | 20.8| 
-| PRIMERA-hf | 41.7 |13.6 | 20.5|
-| PRIMERA(finetuned) | 49.9 | 21.1 | 25.9|
-| PRIMERA-hf(finetuned) | 49.9 | 20.9 | 25.8|
-
-You can use it by 
-```
-from transformers import (
-    AutoTokenizer,
-    LEDConfig,
-    LEDForConditionalGeneration,
-)
-tokenizer = AutoTokenizer.from_pretrained('allenai/PRIMERA')
-config=LEDConfig.from_pretrained('allenai/PRIMERA')
-model = LEDForConditionalGeneration.from_pretrained('allenai/PRIMERA')
-```
+---
+
+language: en
+
+license: apache-2.0
+
+---
+
+
+# 🏬QAmden🏬: Question-Answering-based Multi-DocumENt model
+
+HF-version of the QAmden model fine-tuned over multinews: *Peek Across*: Improving Multi-Document Modeling via Cross-Document Question-Answering (ACL 2023). 
+
+
+You can use it by 
+
+```python
+from transformers import (
+    AutoTokenizer,
+    LEDConfig,
+    LEDForConditionalGeneration,
+)
+# load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('biu-nlp/QAmden')
+config=LEDConfig.from_pretrained('biu-nlp/QAmden-multinews')
+
+model = LEDForConditionalGeneration.from_pretrained('biu-nlp/QAmden-multinews')
+```
+
+The original repo is [here](https://github.com/aviclu/peekacross).
+
+If you find our work useful, please cite the paper as:
+
+```python
+@article{caciularu2023peekacross,
+  title={Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering},
+  author={Caciularu, Avi and Peters, Matthew E and Goldberger, Jacob and Dagan, Ido and Cohan, Arman},
+  journal={The 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023},
+  year={2023}
+}
+```