Update SemEval2018Task7.py

SemEval2018Task7.py

@@ -45,13 +45,25 @@ _CITATION = """\
     url = "https://aclanthology.org/S18-1111",
     doi = "10.18653/v1/S18-1111",
     pages = "679--688",
-    abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.",
+    abstract = "This paper describes the first task on semantic relation extraction and classification in 
+    scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations 
+    and includes three different subtasks. The subtasks were designed so as to compare and quantify the 
+    effect of different pre-processing steps on the relation classification results. We expect the task to 
+    be relevant for a broad range of researchers working on extracting specialized knowledge from domain 
+    corpora, for example but not limited to scientific or bio-medical information extraction. The task 
+    attracted a total of 32 participants, with 158 submissions across different scenarios.",
 }
 """
 
 # You can copy an official description
 _DESCRIPTION = """\
-This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.
+This paper describes the first task on semantic relation extraction and classification in scientific paper
+abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three 
+different subtasks. The subtasks were designed so as to compare and quantify the effect of different
+pre-processing steps on the relation classification results. We expect the task to be relevant for a broad 
+range of researchers working on extracting specialized knowledge from domain corpora, for example but not 
+limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, 
+with 158 submissions across different scenarios.
 """
 
 # Add a link to an official homepage for the dataset here