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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 a variety of test benchmarks.

For more information on our approach check out:

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 functions to extract just the main article content or the content and any user generated comments. Each function takes an HTML string and returns the content string.

import requests
from dragnet import extract_content, extract_content_and_comments

# fetch HTML
url = ''
r = requests.get(url)

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

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

We also provide a sklearn-style extractor class(complete with fit and predict methods). You can either train an extractor yourself, or load a pre-trained one:

from dragnet.util import load_pickled_model

content_extractor = load_pickled_model(
content_comments_extractor = load_pickled_model(
content = content_extractor.extract(r.content)
content_comments = content_comments_extractor.extract(r.content)

A note about encoding

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

content = content_extractor.extract(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, with support recently added for 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.

Installing with Docker:

This is the easiest method to install Dragnet and builds a Docker container with Dragnet and its dependencies.

  1. Install Docker.
  2. Clone the master branch: git clone
  3. Build the docker container: docker build -t dragnet .
  4. Run the tests: docker run dragnet make test

You can also run an interactive Python session:

docker run -ti dragnet python3

Installing without Docker

  1. Install the dependencies needed for Dragnet. The build depends on GCC, numpy, Cython and lxml (which in turn depends on libxml2). We use to setup the dependencies in the Docker container, so you can use it as a template and modify as appropriate for your operation system.
  2. Clone the master branch: git clone
  3. Install the requirements: cd dragnet; pip install -r requirements.txt
  4. Build dragnet:
$ cd dragnet
$ make install
# these should now pass
$ make test


We love contributions! Open an issue, or fork/create a pull request.

More details about the code structure

The Extractor class 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 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.

Training/test data

The training and test data is available at dragnet_data.

Training content extraction models

  1. 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).

  2. 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_all_gold_standard_data
    rootdir = '/path/to/dragnet_data/'

    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.

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

    • 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.extractor import Extractor
    from dragnet.model_training import train_model
    from sklearn.ensemble import ExtraTreesClassifier
    rootdir = '/path/to/dragnet_data/'
    features = ['kohlschuetter', 'weninger', 'readability']
    to_extract = ['content', 'comments']   # or ['content']
    model = ExtraTreesClassifier(
    base_extractor = Extractor(
    extractor = train_model(base_extractor, rootdir)

    This trains the model and, if a value is passed to output_dir, writes a pickled version of it along with some some block level classification errors to a file in the specified output_dir. If no output_dir is specified, the block-level performance is printed to stdout.

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

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

Evaluating content extraction models

Use evaluate_models_predictions in model_training to compute the token level accuracy, precision, recall, and F1. For example, to evaluate a trained model run:

from dragnet.compat import train_test_split
from dragnet.data_processing import prepare_all_data
from dragnet.model_training import evaluate_model_predictions

rootdir = '/path/to/dragnet_data/'
data = prepare_all_data(rootdir)
training_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

test_html, test_labels, test_weights = extractor.get_html_labels_weights(test_data)
train_html, train_labels, train_weights = extractor.get_html_labels_weights(training_data), train_labels, weights=train_weights)
predictions = extractor.predict(test_html)
scores = evaluate_model_predictions(test_labels, predictions, test_weights)

Note that this is the same evaluation that is run/printed in train_model