Multi-document summarization tool relying on ILP and sentence fusion
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Potara is a multi-document summarization system that relies on Integer Linear Programming (ILP) and sentence fusion.

Its goal is to summarize a set of related documents in a few sentences. It proceeds by fusing similar sentences in order to create sentences that are either shorter or more informative than those found in the documents. It then uses ILP in order to choose the best set of sentences, fused or not, that will compose the resulting summary.

It relies on state-of-the-art (as of 2014) approaches introduced by Gillick and Favre for the ILP strategy, and Filippova for the sentence fusion.


You can install most python dependencies with pip

$ pip install -r requirements.txt

You will also need GLPK, which is used to obtain an optimal summary (example for Debian-based distro)

$ sudo apt-get install glpk

For Ubuntu-based distros you can use:

$ sudo apt-get install libglpk40

You may also need to install scipy and numpy with your distro package manager

$ sudo apt-get install python-numpy python-scipy

You can check that the install run successfully by running

$ python test

If you have issues with install, you can check the .travis.yml file of the repo, which corresponds to a working build on Ubuntu 14.04.

How To

Basically, you can use the following

from summarizer import Summarizer
import document

s = Summarizer()
print("Adding docs")
s.setDocuments([document.Document('data/' + str(i) + '.txt')
       for i in range(1,10)])

There's some preprocessing involved and a sentence fusion step, but I made it easily tunable. Preprocessing may take a while (a few minutes) since there is a lot going on under the hood. Default parameters are currently set for summarizing ~10 documents. You can summarize a smaller amount of documents by tweaking the "minbigramcount" parameter of the summarizer :

s = Summarizer(minbigramcount=2)

Summarizing less than 4 documents would probably yield a bad summary.