update doc

Readme.md

@@ -21,21 +21,22 @@ GROBID is a machine learning library for extracting, parsing and re-structuring
 The following functionalities are available:
 
 - __Header extraction and parsing__ from article in PDF format. The extraction here covers the usual bibliographical information (e.g. title, abstract, authors, affiliations, keywords, etc.).
-- __References extraction and parsing__ from articles in PDF format, around .85 f-score against a PubMed Central evaluation set. All the usual publication metadata are covered.
+- __References extraction and parsing__ from articles in PDF format, around .85 f-score against on an independent PubMed Central set of 1943 PDF containing 90,125 references. All the usual publication metadata are covered (including DOI).
 - __Citation contexts recognition and linking__ to the full bibliographical references of the article. The accuracy of citation contexts resolution is around 0.75 f-score (which corresponds to both the correct identification of the citation callout and its correct association with a full bibliographical reference).
-- Parsing of __references in isolation__ (with around 0.89 f-score).
+- Parsing of __references in isolation__ (around 0.89 f-score).
 - __Parsing of names__ (e.g. person title, forenames, middlename, etc.), in particular author names in header, and author names in references (two distinct models).
 - __Parsing of affiliation and address__ blocks.
 - __Parsing of dates__, ISO normalized day, month, year.
 - __Full text extraction and structuring__ from PDF articles, including a model for the overall document segmentation and models for the structuring of the text body (paragraph, section titles, reference callout, figure, table, etc.).
 - __Consolidation/resolution of the extracted bibliographical references__ using the [biblio-glutton](https://github.com/kermitt2/biblio-glutton) service or the [CrossRef REST API](https://github.com/CrossRef/rest-api-doc). In both cases, DOI resolution performance is higher than 0.95 f-score from PDF extraction.
 - __Extraction and parsing of patent and non-patent references in patent__ publications.
+- __PDF coordinates__ for extracted information, allowing to create "augmented" interactive PDF.
 
-In a complete PDF processing, GROBID manages 55 final labels used to build relatively fine-grained structures, from traditional publication metadata (title, author first/last/middlenames, affiliation types, detailed address, journal, volume, issue, pages, etc.) to full text structures (section title, paragraph, reference markers, head/foot notes, figure headers, etc.).
+In a complete PDF processing, GROBID manages 55 final labels used to build relatively fine-grained structures, from traditional publication metadata (title, author first/last/middlenames, affiliation types, detailed address, journal, volume, issue, pages, doi, pmid, etc.) to full text structures (section title, paragraph, reference markers, head/foot notes, figure headers, etc.).
 
 GROBID includes a comprehensive web service API, batch processing, a JAVA API, a Docker image, a generic evaluation framework (precision, recall, etc., n-fold cross-evaluation) and the semi-automatic generation of training data.
 
-GROBID can be considered as production ready. Deployments in production includes ResearchGate, HAL Research Archive, the European Patent Office, INIST-CNRS, Mendeley, CERN (Invenio), and many more. The tool is designed for high scalability in order to address the full scientific literature corpus.
+GROBID can be considered as production ready. Deployments in production includes ResearchGate, HAL Research Archive, the European Patent Office, INIST-CNRS, Mendeley, CERN (Invenio), scite.ai, and many more. The tool is designed for high scalability in order to address the full scientific literature corpus.
 
 GROBID should run properly "out of the box" on Linux (64 bits), MacOS, and Windows (32 and 64 bits).
 
@@ -59,7 +60,7 @@ For helping to exploit GROBID service at scale, we provide clients written in Py
 
 All these clients will take advantage of the multi-threading for scaling large set of PDF processing. As a consequence, they will be much more efficient than the [batch command lines](https://grobid.readthedocs.io/en/latest/Grobid-batch/) (which use only one thread) and should be prefered. 
 
-We have been able recently to run the complete fulltext processing at around 10.6 PDF per second (around 915,000 PDF per day, around 20M pages per day) with the node.js client listed above during one week on a 16 CPU machine (16 threads, 32GB RAM, no SDD, articles from mainstream publishers), see [here](https://github.com/kermitt2/grobid/issues/443#issuecomment-505208132) (11.3M PDF were processed in 6 days by 2 servers without crash).
+We have been able recently to run the complete fulltext processing at around 10.6 PDF per second (around 915,000 PDF per day, around 20M pages per day) with the node.js client listed above during one week on one 16 CPU machine (16 threads, 32GB RAM, no SDD, articles from mainstream publishers), see [here](https://github.com/kermitt2/grobid/issues/443#issuecomment-505208132) (11.3M PDF were processed in 6 days by 2 servers without crash).
 
 In addition, a Java example project is available to illustrate how to use GROBID as a Java library: [https://github.com/kermitt2/grobid-example](https://github.com/kermitt2/grobid-example). The example project is using GROBID Java API for extracting header metadata and citations from a PDF and output the results in BibTeX format.  
 
@@ -97,7 +98,7 @@ ej-technologies provided us a free open-source license for its Java Profiler. Cl
 
 ## Reference
 
-For citing this work, you can refer to the present GitHub project, together with the [Software Heritage](https://www.softwareheritage.org/) project-level permanent identifier. For example, with BibTeX:
+For citing this work, please refer to the present GitHub project, together with the [Software Heritage](https://www.softwareheritage.org/) project-level permanent identifier. For example, with BibTeX:
 
 ```bibtex
 @misc{GROBID,