EXcite-Docker: Tool for the annotation of training material for ML-based reference extraction and segmentation

Note: This repository is no longer maintained. Current work has shifted to https://github.com/cboulanger/anystyle-workflow

This is a docker image that provides a web application to produce training material for two ML-based reference extraction & segmentation engines:

• EXParser
• AnyStyle

Both serve to extract citation data from PDF Documents.

The image provides a Web UI for producing training material which is needed to improve citation recognition for particular corpora of scholarly literature where the current models does not perform well; and provides a CLI to run the EXcite commands, manage multiple sets of model training data and model data, and support an evaluation workflow that can measure the performance of the model. The AnyStyle toolkit has its own CLI and evaluation built in. Currently, support for AnyStyle is limited to editing .ttx documents, more comprehensive integration will follow.

The code has been forked from https://git.gesis.org/hosseiam/excite-docker, but there is little of the original code left except the core EXparser algorithm.

A demo of the web frontend (without backend functionality) is available here.

Installation

1. Install Docker
2. Clone this repo with: git clone https://github.com/cboulanger/excite-docker.git && cd excite-docker
3. Build docker image: ./bin/build
4. If you want to use AnyCite, please consult its GitHub page on how to install it: https://github.com/inukshuk/anystyle

Use of the web frontend

1. Run server: ./bin/start-servers
2. Open frontend at http://127.0.0.1:8000/web/index.html
3. Click on "Help" for instructions (also lets you download the Zotero add-ons)

CLI

You can control the extraction and segmentation process via the CLI. CLI commands can be executed with ./bin/run <command> Available commands can be listed with ./bin/run --help, and you can always get detailed help on each command with ./bin/run <command> [<subcommand>] --help

The main commands for extracting references from PDFs are:

• layout: run layout analysis of any PDF file in Data/1-pdfs and put the result into Data/2-layout
• exparser: process all the files in Data/2-layout. The output will be provided in csv (plain text), xml and BibTex format in the directories Data/3-refs, Data/3-refs_seg and Data/3-refs_bibtex
• segmentation: process the references that are in the csv files in the Data/3-refs directory and output xml and BibTex files in Data/3-refs_seg and Data/3-refs_bibtex

For more CLI commands, see the sections below.

Training a new model

If you want to use this feature, you need to have git-lfs installed before you check out this repository. git-lfs is necessary to download the large files that are used during training.

In order to train a new model from scratch, you need to do the following:

1. Run ./bin/run model create <model_name>
2. Put the PDFs with which you are going to train the model into Data/1-pdfs if they are native PDFs or contain an OCR layer. If the PDFs consist of scanned pages without the OCR layer, put them into 0-pdfs_no_ocr and wait for the OCR server to process them and move them to Data/1-pdfs
3. Create the layout files with ./bin/run layout
4. Move files from Data/2-layout into Dataset/<model_name>/LYT
5. Load the web application and choose your new model from the "Model" dropdown
6. Use the web application to load and annotate the layout files from Dataset/<model_name>/LYT in the identification and segmentation views. Here is more information on training the reference extraction model and the the reference parsing model.
7. "Save" the training files after each annotation, they will be stored in the model directory
8. On the command line, run ./bin/train <model_name>. If you want to train extraction, segmentation and model completeness separately, use ./bin/run train extraction <model_name>, `train segmentation ` or `train completeness `

Training data lives in the Dataset/<model_name> folder. For details, see here.

For training, you need to populate the following folders with training data:

Dataset/<model_name>/LYT/ - layout files
Dataset/<model_name>/LRT/ - layout files with annotation for references <ref>
Dataset/<model_name>/SEG/ - segmentation data for citations 

To run the training, execute ./bin/training <model_name>.

This will generate data in the following folders:

#feature extraction output
Dataset/<model_name>/Features/
Dataset/<model_name>/RefLD/

#model training output
Models/<model_name>/SMN.npy
Models/<model_name>/FSN.npy
Models/<model_name>/rf.pkl - the model

You can list all existing models with bin/run model list and delete a model with bin/run model delete <model_name>.

WebDAV-based model package repository

You can store model and training data on a WebDAV server, which is particularly useful for sharing data and collaborative training. To enable this, rename /.env.dist to .env and configure the required environment variables.

The available CLI commands can be listed with bin/run package --help. To upload training or model data to the WebDAV server, you can use the package publish command, which has the following syntax

bin/run package publish --help
usage: package publish [-h] [--model-name MODEL_NAME] [--trained-model]
                    [--training-data {extraction,segmentation,all}]
                    [--overwrite]
                    package_name

positional arguments:
  package_name          Name of the package in which to publish the model data

optional arguments:
  -h, --help            show this help message and exit
  --model-name MODEL_NAME, -n MODEL_NAME
                        Name of the model from which to publish data. If not
                        given, the name of the package is used.
  --trained-model, -m   Include the trained model itself
  --training-data {extraction,segmentation,all}, -t {extraction,segmentation,all}
                        The type of training data to include in the package
  --overwrite, -o       Overwrite an existing package

The package_name is an arbitrary string which should express the content of the package, plus ideally a timestamp, such as foo-segm-train-data-20220502 or foo-model-data-20220502_075523. You can choose to upload training data with the --training-data option, which takes either "extraction", "segmentation", or "all" for both. To share the trained model itself, use --trained-model. Since the model files are large, this will add significantly to the size of the package and to the time it takes to upload and download the model data. On the other hand, this saves the time for training the model with the training data first.

You can then later bin/run package import <package_name> to import the package contents into a model with the same name, which is created if it does not exist. If you want to import the package contents into a differnt model, specify its name with the --model-name option.

Display the list of remotely stored packages with bin/run package list and delete a package with bin/run package delete <package_name>.

Evaluating the performance of a model

To measure the accuracy of a model, we support the following split - train - eval workflow via scripts that use the CLI commands.

1) bin/split foo foo_split: The training data of a model "foo" is split into 80% training data and 20% evaluation data and moved into a newly created model "foo_split":

2) bin/train foo_split: The model is trained with its training data

3) bin/eval foo_split: Extraction and segmentation is run on the evaluation data and the result is evaluated against the known gold standard.

4) bin/run report foo_split prints the accuracy data to the console (it can also output it to a csv file)

This workflow can be further automated with the `bin/split_train_eval