A simple tool used to extract an article's text in html documents.
A python package for extracting article text in html documents. Check out the new twitter-bootstrap-ready demo produced by the new extraction algorithm!
Check out my latest project: autocomplete - a kid and adult friendly exercise in machine learning
Read Matthew Peters’s article that benchmarked Eatiht, along with few other content extractors written in Python.
tl;dr: Eatiht’s etv2 is fast, but not so accurate (my own research suggests that the original algo is more reliable).
Check out eatiht’s website where I walk through each step in the original algorithm!
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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
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. tree.bootstrapify() 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?
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 first_subtree.get_html() # <Element div at 0x2f88cc8> first_subtree.get_html().tag # '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 # extracted text results # frequency distribution (histogram) results # subtrees (list of textnodes pre-filter) results # pruned subtrees results # 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:directory to your “path” so that the command line tool works from any directory, not only the ..directory.
lxml *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:
boilerpipe - Boilerplate Removal and Fulltext Extraction from HTML pages. Java library written by Christian Kohlschütter
“The Easy Way to Extract Useful Text from Arbitrary HTML” - a Python tutorial on implementing a neural network for html content extraction. Written by alexjc
Pyteaser’s Cleaners module - from what I can tell, it’s a purely heuristic-based process
“Text Extraction from the Web via Text-to-Tag Ratio” - a thesis on Text-to-Tag-heuristic driven clustering as a solution for the problem at hand. Written by Tim Weninger & William H. Hsu
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:
XPath’s select all (//), parent node (..) queries and functions (‘string-length’) are remarkably powerful when used together
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
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
etv2 command line scripts
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)
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