func main() {
ext := model.NewExtractor()
for _, input := range util.GetInput(os.Args[1:]) {
if document, err := html.NewDocument(input.Data); err == nil {
if article, err := ext.Extract(document); err == nil {
// Extraction might miss the article heading. So if the text
// doesn't start with a heading, use the article title as
// opening heading.
if !article.StartsWithHeading() && article.Title != "" {
article.Prepend(util.Heading(article.Title))
}
printArticle(article)
}
}
input.Data.Close()
}
}
// Extract returns a list of relevant text chunks found in doc.
//
// How it works
//
// This function creates a feature vector for each chunk found in doc.
// A feature vector contains a numerical representation of the chunk's
// properties like HTML element type, parent element type, number of words,
// number of sentences and stuff like this.
//
// A logistic regression model is used to calculate scores based on these
// feature vectors. Then, in some kind of meta / ensemble learning approach,
// a second type of feature vector is created based on these scores.
// This feature vector is fed to our random forest and finally
// the random forest's predictions are used to generate the result.
//
// By now you might have noticed that I'm exceptionally bad at naming and
// describing things properly.
func (ext *Extractor) Extract(doc *html.Document) (*util.Article, error) {
*ext = Extractor{}
if len(doc.Chunks) == 0 {
return nil, ErrNoChunks
}
chunkFeatures := make([]chunkFeature, len(doc.Chunks))
boostFeatures := make([]boostFeature, len(doc.Chunks))
// Count the number of words and sentences we encountered for each
// class. This helps us to detect elements that contain the doc text.
classStats := doc.GetClassStats()
clusterStats := doc.GetClusterStats()
chunkFeatureWriter := new(chunkFeatureWriter)
for i, chunk := range doc.Chunks {
chunkFeatureWriter.Assign(chunkFeatures[i][:])
chunkFeatureWriter.WriteElementType(chunk)
chunkFeatureWriter.WriteParentType(chunk)
chunkFeatureWriter.WriteSiblingTypes(chunk)
chunkFeatureWriter.WriteAncestors(chunk)
chunkFeatureWriter.WriteTextStat(chunk)
chunkFeatureWriter.WriteTextStatSiblings(chunk)
chunkFeatureWriter.WriteClassStat(chunk, classStats)
chunkFeatureWriter.WriteClusterStat(chunk, clusterStats)
}
// Detect the minimum and maximum value for each element in the
// feature vector.
empMin := chunkFeature{}
empMax := chunkFeature{}
for i := range chunkFeatures {
for j, val := range chunkFeatures[i] {
switch {
case val < empMin[j]:
empMin[j] = val
case val > empMax[j]:
empMax[j] = val
}
}
}
// Perform MinMax normalization.
for i := range chunkFeatures {
feature := &chunkFeatures[i]
for j, val := range chunkFeatures[i] {
// If the maximum value is not greater than one, we assume that the feature is
// already normalized and leave it untouched.
if empMax[j] > 1.0 {
feature[j] = (val - empMin[j]) / (empMax[j] - empMin[j])
}
}
}
// Now cluster chunks by containers to calculate average score per
// container.
clusterContainer := newClusterMap()
for i, chunk := range doc.Chunks {
clusterContainer.Add(chunk.Container, chunk, chunkFeatures[i].Score())
}
boostFeatureWriter := new(boostFeatureWriter)
for i, chunk := range doc.Chunks {
boostFeatureWriter.Assign(boostFeatures[i][:])
boostFeatureWriter.WriteChunk(chunk)
boostFeatureWriter.WriteCluster(chunk, clusterContainer[chunk.Container])
boostFeatureWriter.WriteTitleSimilarity(chunk, doc.Title)
}
// Cluster chunks by block.
clusterBlock := newClusterMap()
for i, chunk := range doc.Chunks {
clusterBlock.Add(chunk.Block, chunk, boostFeatures[i].Score(), float32(chunk.Text.Len()))
}
// Label all chunks whose blocks have a score above prediction level.
// This makes sure that we don't split large blocks.
ext.Labels = make([]bool, len(doc.Chunks))
for i, chunk := range doc.Chunks {
if cluster, ok := clusterBlock[chunk.Block]; ok {
ext.Labels[i] = cluster.Score() > 0.5
}
}
result := &util.Article{Title: doc.Title.String()}
for i, chunk := range doc.Chunks {
if cluster, ok := clusterBlock[chunk.Block]; ok && ext.Labels[i] {
text := util.NewText()
for _, chunk := range cluster.Chunks {
text.WriteText(chunk.Text)
}
if chunk.IsHeading() {
result.Append(util.Heading(text.String()))
} else {
result.Append(util.Paragraph(text.String()))
}
delete(clusterBlock, chunk.Block)
}
}
if len(result.Text) == 0 {
return nil, ErrEmptyResult
}
return result, nil
}