Dragnet isn't interested in the shiny chrome or boilerplate dressing of a web page. It's interested in... 'just the facts.' The machine learning models in Dragnet extract the main article content and optionally user generated comments from a web page. They provide state of the art performance on variety of test benchmarks.

For more information on our approach check out:

• Our paper Content Extraction Using Diverse Feature Sets, published at WWW in 2013, gives an overview of the machine learning approach.
• A comparison of Dragnet and alternate content extraction packages.
• This blog post explains the intuition behind the algorithms.

This project was originally inspired by Kohlschütter et al, Boilerplate Detection using Shallow Text Features and Weninger et al CETR -- Content Extraction with Tag Ratios, and more recently by Readability.

Depending on your use case, we provide two separate models to extract just the main article content or the content and any user generated comments. Each model implements the analyze method that takes an HTML string and returns the content string.

import requests
from dragnet import content_extractor, content_comments_extractor

# fetch HTML
url = 'https://moz.com/devblog/dragnet-content-extraction-from-diverse-feature-sets/'
r = requests.get(url)

# get main article without comments
content = content_extractor.analyze(r.content)

# get article and comments
content_comments = content_comments_extractor.analyze(r.content)

We also provide some additional models in dragnet.models but don't recommend their use for anything other than academic curiousity.

If you know the encoding of the document (e.g. from HTTP headers), you can pass it down to the parser:

content = content_extractor.analyze(html_string, encoding='utf-8')

Otherwise, we try to guess the encoding from a meta tag or specified <?xml encoding=".."?> tag. If that fails, we assume "UTF-8".

Dragnet is written in Python (developed with 2.7, not tested on 3) and built on the numpy/scipy/Cython numerical computing environment. In addition we use lxml (libxml2) for HTML parsing.

We recommend installing from the master branch to ensure you have the latest version.

This is the easiest method to install Dragnet and builds a Vagrant virtual machine with Dragnet and it's dependencies.

1. Install vagrant.
2. Install Virtual Box.
3. Clone the master branch: git clone git@github.com:seomoz/dragnet.git
4. Bring up the vagrant box: vagrant up
5. Log into the vagrant box:

vagrant ssh
# inside the vagrant vm
$ cd /vagrant
# these should now pass
$ make test

1. Install the dependencies need for Dragnet. The build depends on numpy, Cython and lxml (which in turn depends on libxml2). We use provision.sh to provision the Vagrant VM so you can use it as a template and modify as appropriate for your operation system.
2. Clone the master branch: git clone git@github.com:seomoz/dragnet.git
3. Install the requirements: sudo pip install -r dragnet/requirements.txt
4. Build dragnet

$ cd dragnet
$ sudo make install
# these should now pass
$ make test

We love contributions! We are especially looking for someone who would like to work on a Python 3 port. Open an issue, or fork/create a pull request.

Each of the models in dragnet.models implements the content extraction model interface defined in ContentExtractionModel. A content extraction model encapsulates a blockifier, some feature extractors and a machine learning model.

A blockifier implements blockify that takes a HTML string and returns a list of block objects. A feature extractor is a callable that takes a list of blocks and returns a numpy array of features (len(blocks), nfeatures). There is some additional optional functionality to "train" the feature (e.g. estimate parameters needed for centering) specified in features.py. The machine learning model implements the scikits-learn interface (predict and fit) and is used to compute the content/no-content prediction for each block.

The training and test data is available at dragnet_data.

0. Download the training data (see above). In what follows ROOTDIR contains the root of the dragnet_data repo, another directory with similar structure (HTML and Corrected sub-directories).

1. Create the block corrected files needed to do supervised learning on the block level. First make a sub-directory $ROOTDIR/block_corrected/ for the output files, then run:

   from dragnet.data_processing import extract_gold_standard_all_training_data
   rootdir = '/path/to/dragnet_data/'
   extract_gold_standard_all_training_data(rootdir)
   

   This solves the longest common sub-sequence problem to determine which blocks were extracted in the gold standard. Occasionally this will fail if lxml (libxml2) cannot parse a HTML document. In this case, remove the offending document and restart the process.

2. Use k-fold cross validation in the training set to do model selection and set any hyperparameters. Make decisions about the following:

   • Number of folds (recommend 5)
   • Whether to use just article content or content and comments.
   • The features to use
   • The machine learning model to use

   For example, to train the randomized decision tree classifier from sklearn using the shallow text features from Kohlschuetter et al. and the CETR features from Weninger et al.:

   from dragnet.model_training import DragnetModelTrainer, accuracy_auc
   from dragnet.data_processing import DragnetModelData
   from sklearn.ensemble import ExtraTreesClassifier
   
   datadir = '/path/to/dragnet_data/'
   
   kfolds = 5
   # recommend using weights but the model.fit methods needs to support it
   weighted = True
   features_to_use = ['kohlschuetter', 'weninger']
   
   content_or_comments = 'both'   # or 'content'
   
   model_library = [
       [ExtraTreesClassifier, accuracy_auc, None, (),
           {'n_estimators':10, 'max_features': None,
            'min_samples_leaf':75}]
   ]
   
   data = DragnetModelData(datadir)
   trainer = DragnetModelTrainer(content_or_comments=content_or_comments,
       weighted=weighted, kfolds=kfolds)
   
   errors, features, labels, weights, folds = trainer.train_model(
       data, model_library, features_to_use)
   

   This trains the model and writes a pickled version of it along with some some block level classification errors to a file.

3. Once you have decided on a final model, train it on the entire training data using dragnet.model_training.train_models.

4. As a last step, test the performance of the model on the test set (see below).

Use evaluate_models_tokens in model_training to compute the token level precision, recall and F1. For example, to evaluate the baseline model (keep everything) run:

from dragnet.model_training import evaluate_models_tokens
from dragnet.models import baseline_model

rootdir = '/path/to/dragnet_data/'
scores = evaluate_models_tokens(rootdir, baseline_model)