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[He 2012] Document Summarization based on Data Reconstruction
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
class DSDR:
"""Z He, et al. Document Summarization based onData Reconstruction (2012)
http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewPaper/4991
"""
@staticmethod
def lin(V, m, lamb):
'''DSDR with linear reconstruction
Parameters
==========
- V : 2d array_like, the candidate data set
- m : int, the number of sentences to be selected
- lamb : float, the trade off parameter
Returns
=======
- L : list, the set of m summary sentences indices
'''
L = []
V = np.array(V)
B = np.dot(V, V.T) / lamb
n = len(V)
for t in range(m):
scores = []
for i in range(n):
score = np.sum(B[:,i] ** 2) / (1. + B[i,i])
scores += [(score, i)]
max_score, max_i = max(scores)
L += [max_i]
B = B - np.outer(B[:,max_i], B[:,max_i]) / (1. + B[max_i,max_i])
return L
@staticmethod
def non(V, gamma, eps=1.e-8):
'''DSDR with nonnegative linear reconstruction
Parameters
==========
- V : 2d array_like, the candidate sentence set
- gamma : float, > 0, the trade off parameter
- eps : float, for converge
Returns
=======
- beta : 1d array, the auxiliary variable to control candidate sentences
selection
'''
V = np.array(V)
n = len(V)
A = np.ones((n,n))
beta = np.zeros(n)
VVT = np.dot(V, V.T) # V * V.T
np.seterr(all='ignore')
while True:
_beta = np.copy(beta)
beta = (np.sum(A ** 2, axis=0) / gamma) ** .5
while True:
_A = np.copy(A)
A *= VVT / np.dot(A, VVT + np.diag(beta))
A = np.nan_to_num(A) # nan (zero divide by zero) to zero
if np.sum(A - _A) < eps: break
if np.sum(beta - _beta) < eps: break
return beta
if __name__ == '__main__':
pass
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from dsdr import DSDR
class TestDSDR(unittest.TestCase):
def test_lin(self):
'''
>>> DSDR.lin(V, m=2, lamb=0.1)
[2, 4]
'''
V = [[1,0,0,0],
[0,1,0,0],
[1,1,0,0],
[0,0,1,0],
[0,0,1,1]]
L = DSDR.lin(V, m=2, lamb=0.1)
print L
# show summary
for i in L:
print V[i]
def test_non(self):
'''
>>> DSDR.non(V, gamma=0.1)
[ 0.49301097 0.49301097 0.6996585 0.49301097 0.70211699]
'''
V = [[1,0,0,0],
[0,1,0,0],
[1,1,0,0],
[0,0,1,0],
[0,0,1,1]]
beta = DSDR.non(V, gamma=0.1)
print beta
# show summary
for score, v in sorted(zip(beta, V), reverse=True):
print score, v
if __name__ == '__main__':
unittest.main()
@Oluwajava

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commented Sep 8, 2015

Thank you...but please how can i pass text to it to make summarize the text for me instead of 0, 1 there

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