A simple tool used to extract an article's text in html documents.

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pip install eatiht==0.1.14


A word about this repo

@eugene-eeo and I are reimagining web data extraction: libextract. It supports not only article extraction, but also tabular data extraction!

That said, it's very unlikely that I will be pushing any updates to this branch, but I will continue to accept pull requests.

Hope to see you at libextract,

Rodrigo :)


A python package for extracting article text in html documents. Check out the new twitter-bootstrap-ready demo produced by the new extraction algorithm!

###Latest News

Check out my latest project: autocomplete - a kid and adult friendly exercise in machine learning

I'm collaborating with Tim Weninger in a must-read data-driven opinion piece (publish date is tba). I benchmarked Eatiht and many more content extractors; you can follow the current work here!.

Read Matthew Peters's article that benchmarked Eatiht, along with few other content extractors written in Python.

Follow me on twitter :)

###What people have been saying

You should write a paper on this work - /u/queue_cumber

This is neat-o. A short and sweet project... - /u/CandyCorns_

This is both useful and shows a simple use case for data mining for the general population - an outreach of sorts. - /u/tweninger

At a Glance

To install:

pip install eatiht
easy_install eatiht

Note: On Windows, you may need to install lxml manually using: pip install lxml

Using in Python

Currently, there are two new submodules:

  • etv2.py - class-based approach

  • v2.py - script-like approach

As requested, etv2.extract will extract not only the text, but also the parent element's html:

import eatiht.etv2 as etv2

url = "http://sputniknews.com/middleeast/20141225/1016239222.html"

tree = etv2.extract(url)

# we know what this does...
# print tree.get_text()

# add necessary link tags to bootstrap cdn, center content, etc.

print tree.get_html_string()


<html><head><title>Syrian Army Kills Nearly 5,000 IS Militants in Three Months: Source / Sputnik International</title>
<link href="//maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css" type="text/css" rel="stylesheet"></head>
<body><h2>Syrian Army Kills Nearly 5,000 IS Militants in Three Months: Source / Sputnik International</h2>...

Now what about if that's rendered?

With boostrap


etv2 uses classes defined in eatiht_trees.py to construct what is sometimes known as the "state space" in the world of AI. But instead of only keeping track of averages and totals - as is required for the algorithm - the "state" class TextNodeSubTree also keeps a reference to its original lxml.html element from whence it came.

You can access the original, extracted html elements like this:

subtrees = tree.get_subtrees()

first_subtree = subtrees[0]

# <Element div at 0x2f88cc8>

# 'div'

Please refer to eatiht_trees.py for more info on what functions are available for you to use.

v2 is functionally identical to the original eatiht:

import eatiht.v2 as v2

url = 'http://www.washingtonpost.com/blogs/the-switch/wp/2014/12/26/elon-musk-the-new-tesla-roadster-can-travel-some-400-miles-on-a-single-charge/'

print v2.extract(url)


Car nerds, you just got an extra present under the tree.

Tesla announced Friday an upgrade for its Roadster, the electric car company’s convertible model,
and said that the new features significantly boost its range -- beyond what many traditional cars
can get on a tank of gasoline.

v2 contains one extra function that executes the extraction algorithm, but along with returning the text, it also returns the structures that were used to calculate the output (ie. histogram, list of xpaths, etc.):

results = v2.extract_more(url)

results[0]      # extracted text
results[1]      # frequency distribution (histogram)
results[2]      # subtrees (list of textnodes pre-filter)
results[3]      # pruned subtrees
results[4]      # list of paragraphs (as seperated in original website)

Now whether or not this function's output looks messy is up for debate; I personally think it looks messy and difficult to remember which index leads to what.

I suggest using this module if you simply want the extracted text.

And of course, there is the original:

# from initial release
import eatiht

url = 'http://news.yahoo.com/curiosity-rover-drills-mars-rock-finds-water-122321635.html'

print eatiht.extract(url)
NASA's Curiosity rover is continuing to help scientists piece together the mystery of how Mars
lost its surface water over the course of billions of years. The rover drilled into a piece of
Martian rock called Cumberland and found some ancient water hidden within it...

Using as a command line tool:

eatiht http://news.yahoo.com/curiosity-rover-drills-mars-rock-finds-water-122321635.html >> out.txt

Note: Window's users may have to add the C:\PythonXX\Scripts directory to your "path" so that the command line tool works from any directory, not only the ..\Scripts directory.


*requests, as of v0.1.0, is no longer required


After searching through the deepest crevices of the internet for some tool|library|module that could effectively extract the main content from a website (ignoring text from ads, sidebar links, etc.), I was slightly disheartened by the apparent ambiguity caused by this content-extraction problem.

My survey resulted in some of the following solutions:

The number of research papers I found on the subject largely outweighs the number available open-source projects. This is my attempt at balancing out the disparity.

In the process of coming up with a solution, I made two unoriginal observations:

  1. XPath's select all (//), parent node (..) queries and functions ('string-length') are remarkably powerful when used together
  2. Unnecessary machine learning is unnecessary

By making an assumption on sentence length, and this is trivial, one can query for text-nodes satisfying said sentence length, then create a frequency distribution (histogram) across the parent-nodes, and the argmax of the resulting distribution is the xpath that is shared amongst likely sentences.

The results were surprisingly good. I personally prefer this approach to the others as it seems to lie somewhere in between the purely rule-based and the drowning-in-ML approaches.

Issues or Contact

Please raise any issues or yell at me at rodrigopala91@gmail.com or @rodricios


Currently, the tests are lacking. But please still run these tests to ensure that modifications to eatiht.py, v2.py, and etv2.py run properly.

python setup.py test


  • HTML-and-text extraction
  • etv2 command line scripts
  • etv2.py tests
  • improve filtering|pruning step so that taglines from articles get dropped
    • if and only if tagline has a reference image, don't prune
  • add some template engine (see "bootstrapify" function for current state)