public override void Summarize(SummaryParameters mySummaryParameters, string newsDirectory, string cacheFileName)
{
Mis = (ContinuousLexRankParameters)mySummaryParameters;
Debug.WriteLine("Starting execution of ContinuousLexRank.");
var startTime = DateTime.Now;
var myTDM = new TDM(newsDirectory, Mis.MyTDMParameters, cacheFileName);
var normalized = ((ContinuousLexRankParameters)mySummaryParameters).SimilarityNormalized;
var mySimilarityMatrix = new SimilarityMatrix(myTDM, cacheFileName, normalized);
var totalPhrases = myTDM.PhrasesList.Count;
var myCosineSimilarities = mySimilarityMatrix.CosineSimilarityBetweenPhrases;
// Calculate the transition probabilities over the same similarity matrix
// the row vector is normalized |v| = 1
for (var i = 0; i < totalPhrases; i++)
{
var sum = 0.0d;
for (var j = 0; j < totalPhrases; j++)
{
sum += myCosineSimilarities[i][j];
}
// The row must add 1 to be a matrix representing a markov chain where the
// transition probabilities of state i to state j are stored.
// It must add 1 because all the probabilities of change when accumulating must add 1.
for (var j = 0; j < totalPhrases; j++)
{
myCosineSimilarities[i][j] /= sum;
}
}
// The neighborhood graph is constructed - Matrix stochastic, irreducible and aperiodic.
// It is irreducible if a state (node) can be reached from any other state (node).
// It is aperiodic if the period = 1, i.e. mcd {n: P(n) x,x > 0}
for (var i = 0; i < totalPhrases; i++)
{
for (var j = 0; j < totalPhrases; j++)
{
var valor = myCosineSimilarities[i][j];
valor = (Mis.DampingFactor / totalPhrases) +
(1 - Mis.DampingFactor) * valor;
myCosineSimilarities[i][j] = valor;
}
}
// Based on the Perron-Frobenius theorem, an irreducible and aperiodic Markov chain
// always converges to a single stationary distribution.
var weights = UtilLexRank.PowerMethod(myCosineSimilarities, Mis.ErrorTolerance);
var phrasesList = new List <PositionValue>(); // Save candidate phrases with their weight (relevance)
for (var i = 0; i < totalPhrases; i++)
{
phrasesList.Add(new PositionValue(i, weights[i]));
}
//phrasesList.Sort((x,y) => -1*x.Value.CompareTo(y.Value)); // The phrases are ordered by their weight
phrasesList.Sort(delegate(PositionValue x, PositionValue y)
{
if (Math.Abs(x.Value - y.Value) < 1e-07)
{
return(myTDM.PhrasesList[x.Position].PositionInDocument.CompareTo(myTDM.PhrasesList[y.Position].PositionInDocument));
}
return(-1 * x.Value.CompareTo(y.Value));
});
TextSummary = Util.SummarizeByCompressionRatio(myTDM, phrasesList, mySummaryParameters.MySummaryType,
Mis.MaximumLengthOfSummaryForRouge, out SummaryByPhrases);
var endTime = DateTime.Now - startTime;
Debug.WriteLine("Minutes of ContinuousLexRank: " + endTime.TotalMinutes);
}
public override void Summarize(SummaryParameters mySummaryParameters, string newsDirectory, string cacheFileName)
{
Mis = (LexRankWithThresholdParameters)mySummaryParameters;
Debug.WriteLine("Starting execution of LexRankWithThreshold.");
var startTime = DateTime.Now;
var myTDM = new TDM(newsDirectory, Mis.MyTDMParameters, cacheFileName);
var normalized = ((LexRankWithThresholdParameters)mySummaryParameters).SimilarityNormalized;
var mySimilarityMatrix = new SimilarityMatrix(myTDM, cacheFileName, normalized);
var totalPhrases = myTDM.PhrasesList.Count;
var myCosineSimilarities = mySimilarityMatrix.CosineSimilarityBetweenPhrases;
// Calculate the transition probabilities on the same similarity matrix,
// i.e., the values are passed to 1 or 0 if they exceed the threshold and
// at the end it is normalized dividing by the number of ones that remained
// ... the row vector is normalized |v| = 1.
// With this, one property of markov models are met.
for (var i = 0; i < totalPhrases; i++)
{
var sum = 0.0d;
for (var j = 0; j < totalPhrases; j++)
{
if (myCosineSimilarities[i][j] > Mis.Threshold)
{
myCosineSimilarities[i][j] = 1;
sum++;
}
else
{
myCosineSimilarities[i][j] = 0;
}
}
for (var j = 0; j < totalPhrases; j++)
{
myCosineSimilarities[i][j] /= sum;
}
}
// Here the other two properties of the markov matrices are met.
// The neighborhood graph is constructed.
for (var i = 0; i < totalPhrases; i++)
{
for (var j = 0; j < totalPhrases; j++)
{
myCosineSimilarities[i][j] = (Mis.DampingFactor / totalPhrases) +
(1 - Mis.DampingFactor) * myCosineSimilarities[i][j];
}
}
var weights = UtilLexRank.PowerMethod(myCosineSimilarities, Mis.ErrorTolerance);
var phrasesList = new List <PositionValue>(); // Save candidate phrases with their weight (relevance)
for (var i = 0; i < totalPhrases; i++)
{
phrasesList.Add(new PositionValue(i, weights[i]));
}
//phrasesList.Sort((x,y) => -1*x.Value.CompareTo(y.Value)); // The phrases are ordered by their weight
phrasesList.Sort(delegate(PositionValue x, PositionValue y)
{
if (Math.Abs(x.Value - y.Value) < 1e-07)
{
return(myTDM.PhrasesList[x.Position].PositionInDocument.CompareTo(myTDM.PhrasesList[y.Position].PositionInDocument));
}
return(-1 * x.Value.CompareTo(y.Value));
});
TextSummary = Util.SummarizeByCompressionRatio(myTDM, phrasesList, mySummaryParameters.MySummaryType,
Mis.MaximumLengthOfSummaryForRouge, out SummaryByPhrases);
var fin = DateTime.Now - startTime;
Debug.WriteLine("Minutes of LexRankWithThreshold " + fin.TotalMinutes);
}
