Update README.md

README.md

@@ -39,19 +39,19 @@ task_ids: []
 This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
 ](https://arxiv.org/abs/2210.09932)" . 
 
-Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/anlaysis on the content of each dataset are provided in the paper.
+Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/analyses on the content of each dataset are provided in the paper.
 
 Both "elife" and "plos" have 6 features:
 
-    - "article": the body of the document (including the abstract), sections seperated by "/n".
-    - "section_headings": the title of each section, seperated by "/n". 
-    - "keywords": keywords describing the topic of the article, seperated by "/n".
-    - "title" : the title of the article.
-    - "year" : the year the article was published.
+    - "article": the body of the document (including the abstract), sections separated by "/n".
+    - "section_headings": the title of each section, separated by "/n". 
+    - "keywords": keywords describing the topic of the article, separated by "/n".
+    - "title": the title of the article.
+    - "year": the year the article was published.
     - "summary": the lay summary of the document.
   
   
-**Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py" script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
+**Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py` script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
     
 ### Supported Tasks and Leaderboards