update dataset card with tables and better summary

README.md

@@ -68,7 +68,52 @@ task_ids:
 ### Dataset Summary
 
 Elsevier OA CC-By Corpus: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals
-representing a large scale, cross-discipline set of research data to support NLP and ML research.
+representing a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text
+articles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).
+
+***Distribution of Publication Years***
+
+| Publication Year | Number of Articles |
+| :---: | :---: |
+| 2014 | 3018 |
+| 2015 | 4438 |
+| 2016 | 5913 |
+| 2017 | 6419 |
+| 2018 | 8016 |
+| 2019 | 10135 |
+| 2020 | 2159 |
+
+***Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.***
+
+| Discipline | Count |
+| --- | ---: |
+| General | 3847 |
+| Agricultural and Biological Sciences | 4840 |
+| Arts and Humanities | 982 |
+| Biochemistry, Genetics and Molecular Biology | 8356 |
+| Business, Management and Accounting | 937 |
+| Chemical Engineering | 1878 |
+| Chemistry | 2490 |
+| Computer Science | 2039 |
+| Decision Sciences | 406 |
+| Earth and Planetary Sciences | 2393 |
+| Economics, Econometrics and Finance | 976 |
+| Energy | 2730 |
+| Engineering | 4778 |
+| Environmental Science | 6049 |
+| Immunology and Microbiology | 3211 |
+| Materials Science | 3477 |
+| Mathematics | 538 |
+| Medicine | 7273 |
+| Neuroscience | 3669 |
+| Nursing | 308 |
+| Pharmacology, Toxicology and Pharmaceutics | 2405 |
+| Physics and Astronomy | 2404 |
+| Psychology | 1760 |
+| Social Sciences | 3540 |
+| Veterinary | 991 |
+| Dentistry | 40 |
+| Health Professions | 821 |
 
 ### Supported Tasks and Leaderboards
 
@@ -168,7 +213,8 @@ The original dataset was published with the following json structure:
 for the document through the addition of `https//www.sciencedirect.com/science/pii/<docId>`
 
 ***abstract*** This is the author provided abstract for the document
-body_text The full text for the document. The text has been split on sentence boundaries, thus making it easier to
+
+***body_text*** The full text for the document. The text has been split on sentence boundaries, thus making it easier to
 use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and
 IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the
 title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article
@@ -186,7 +232,7 @@ was used.
 ***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and
 PMID). Along with a number of classification schemes such as ASJC and Subject Classification.
 
-***Author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The
+***author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The
 coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with
 the aim of summarising the core findings and results in the article.