Update README.md

README.md

@@ -63,7 +63,8 @@ We acknowledge and cite the CulturaX dataset using the following citation:
    
 Additionally, the dataset includes news article data, and we acknowledge and cite the source of this data using the following citations:
 
-   
+  ```
+ 
    @inproceedings{see-etal-2017-get,
     title = "Get To The Point: Summarization with Pointer-Generator Networks",
     author = "See, Abigail  and
@@ -79,7 +80,7 @@ Additionally, the dataset includes news article data, and we acknowledge and cit
     pages = "1073--1083",
     abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",}
 
-@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
+   @inproceedings{DBLP:conf/nips/HermannKGEKSB15,
   author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
   title={Teaching Machines to Read and Comprehend},
   year={2015},
@@ -89,7 +90,7 @@ Additionally, the dataset includes news article data, and we acknowledge and cit
   booktitle={NIPS},
   crossref={conf/nips/2015}
 }
-
+ ```
 
 #### License