updated load dataset file

scientific_lay_summarization.py

@@ -39,6 +39,14 @@ _DESCRIPTION = """
 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.
+
+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.
+    - "summary": the lay summary of the document.
 """
 
 _DOCUMENT = "article"
@@ -59,7 +67,7 @@ class ScientificLaySummarisationConfig(datasets.BuilderConfig):
           filename: filename of different configs for the dataset.
           **kwargs: keyword arguments forwarded to super.
         """
-        super(ScientificLaySummarisationConfig, self).__init__(version=datasets.Version("1.0"), **kwargs)
+        super(ScientificLaySummarisationConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
         self.filename = filename
 
 
@@ -78,7 +86,7 @@ class ScientificLaySummarisation(datasets.GeneratorBasedBuilder):
                 {
                     _DOCUMENT: datasets.Value("string"),
                     _SUMMARY: datasets.Value("string"),
-                    "section_names": datasets.Value("string"),
+                    "section_headings": datasets.Value("string"),
                     "keywords": datasets.Value("string"),
                     "year": datasets.Value("string"),
                     "title": datasets.Value("string"),
@@ -117,19 +125,22 @@ class ScientificLaySummarisation(datasets.GeneratorBasedBuilder):
                 # "id": str,                      # unique identifier
                 # "year": int,                    # year of publication
                 # "title": str,                   # title
-                # "sections": List[List[str]],    # main text, divided in to sections
+                # "sections": List[List[str]],    # main text, divided in to sections/sentences
                 # "headings" List[str],           # headings of each section
-                # "abstract": List[str],          # abstract
-                # "summary": List[str],           # lay summary
+                # "abstract": List[str],          # abstract, in sentences
+                # "summary": List[str],           # lay summary, in sentences
                 # "keywords": List[str]           # keywords/topic of article
 
                 d = json.loads(line)
-                summary = "\n".join(d["abstract_text"])
+
+                sections = [" ".join(s).strip() for s in d["sections"]]
+                abstract = " ".join(d['abstract']).strip()
+                summary = " ".join(d["summary"]).strip()
 
                 yield d["id"], {
-                    _DOCUMENT: "\n".join([d["abstract"]] + d["sections"]),
+                    _DOCUMENT: "\n".join([[abstract] + sections]),
                     _SUMMARY: summary,
-                    "section_headings": "\n".join(d["headings"]),
+                    "section_headings": "\n".join(["Abstract"] + d["headings"]),
                     "keywords": "\n".join(d["keywords"]),
                     "year": d["year"],
                     "title": d["title"]