Science Parse

Science Parse parses scientific papers (in PDF form) and returns them in structured form. As of today, it supports these fields:

• Title
• Authors
• Abstract
• Sections (each with heading and body text)
• Bibliography, each with
  • Title
  • Authors
  • Venue
  • Year
• Mentions, i.e., places in the paper where bibliography entries are mentioned

In JSON format, the output looks like this (or like this, if you want sections). The easiest way to get started is to use the output from this server.

New version: SPv2

There is a new version of science-parse out that works in a completely different way. It has fewer features, but higher quality in the output. Check out the details at https://github.com/allenai/spv2.

Get started

There are three different ways to get started with SP. Each has its own document:

• Server: This contains the SP server. It's useful for PDF parsing as a service. It's also probably the easiest way to get going.
• CLI: This contains the command line interface to SP. That's most useful for batch processing.
• Core: This contains SP as a library. It has all the extraction code, plus training and evaluation. Both server and CLI use this to do the actual work.

Alternatively, you can run the docker image: docker run -p 8080:8080 --rm allenai/scienceparse:2.0.3

How to include into your own project

The current version is 2.0.3. If you want to include it in your own project, use this:

For SBT:

libraryDependencies += "org.allenai" %% "science-parse" % "2.0.3"

For Maven:

<dependency>
  <groupId>org.allenai</groupId>
  <artifactId>science-parse_2.11</artifactId>
  <version>2.0.3</version>
</dependency>

The first time you run it, SP will download some rather large model files. Don't be alarmed! The model files are cached, and startup is much faster the second time.

For licensing reasons, SP does not include libraries for some image formats. Without these libraries, SP cannot process PDFs that contain images in these formats. If you have no licensing restrictions in your project, we recommend you add these additional dependencies to your project as well:

  "com.github.jai-imageio" % "jai-imageio-core" % "1.2.1",
  "com.github.jai-imageio" % "jai-imageio-jpeg2000" % "1.3.0", // For handling jpeg2000 images
  "com.levigo.jbig2" % "levigo-jbig2-imageio" % "1.6.5", // For handling jbig2 images

Development

This project is a hybrid between Java and Scala. The interaction between the languages is fairly seamless, and SP can be used as a library in any JVM-based language.

Our build system is sbt. To build science-parse, you have to have sbt installed and working. You can find details about that at https://www.scala-sbt.org.

Once you have sbt set up, just start sbt in the main project folder to launch sbt's shell. There are many things you can do in the shell, but here are the most important ones:

• test runs all the tests in all the projects.
• cli/assembly builds a runnable superjar (i.e., a jar with all dependencies bundled) for the project. You can run it (from bash, not from sbt) with java -Xmx10g -jar <location of superjar>.
• server/assembly builds a runnable superjar for the webserver.
• server/run starts the server directly from the sbt shell.

Lombok

This project uses Lombok which requires you to enable annotation processing inside of an IDE. Here is the IntelliJ plugin and you'll need to enable annotation processing (instructions here).

Lombok has a lot of useful annotations that give you some of the nice things in Scala:

• val is equivalent to final and the right-hand-side class. It gives you type-inference via some tricks
• Check out @Data

Thanks

Special thanks goes to @kermitt2, whose work on kermitt2/grobid inspired Science Parse, and helped us get started with some labeled data.