private List<Summerizer[]> generateSummarizerList(
List<Doc> docs, NGramProbs[] probs, LDAProbs inferredProbs) {
List<Summerizer[]> summarizers = new ArrayList<Summerizer[]>();
Summerizer[] s;
// s = new Summerizer[docs.size()];
// for (int i = 0; i < s.length; i++) {
// s[i] = new FirstSentSum(docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH);
// }
// summarizers.add(s);
s = new Summerizer[docs.size()];
for (int i = 0; i < s.length; i++) {
System.out.println("Generating FirstBaseline #" + (i + 1));
s[i] = new FirstBaseline(docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH);
}
summarizers.add(s);
s = new Summerizer[docs.size()];
for (int i = 0; i < s.length; i++) {
System.out.println("Generating SecondBaseline #" + (i + 1));
s[i] = new SecondBaseline(docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH);
}
summarizers.add(s);
// need to change constructors to include corpus TreeMap
// s = new Summerizer[docs.size()];
// for (int i = 0; i < s.length; i++) {
// System.out.println("Generating ArticleTopicNGramSum #" + (i+1));
// s[i] = new ArticleTopicNGramSum(docs.get(i),
// DEFAULT_MAX_SUMMARY_LENGTH);
// }
// summarizers.add(s);
// s = new Summerizer[docs.size()];
// for (int i = 0; i < s.length; i++) {
// System.out.println("Generating NeFreqBasedSum #" + (i+1));
// s[i] = new NeFreqBasedSum(docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH);
// }
// summarizers.add(s);
// s = new Summerizer[docs.size()];
// for (int i = 0; i < s.length; i++) {
// System.out.println("Generating MostProbSentBasedOnTopicDocProb #" + (i+1));
// s[i] = new MostProbSentBasedOnTopicDocProb(docs.get(i),
// DEFAULT_MAX_SUMMARY_LENGTH);
// }
// summarizers.add(s);
CorpusCounts counts;
try {
counts = SerializableWrapper.readObject(CorpusCounts.SAVE_PATH);
} catch (IOException e) {
throw new RuntimeException(e);
}
s = new Summerizer[docs.size()];
for (int i = 0; i < s.length; i++) {
System.out.println("Generating FeatureBased #" + (i + 1));
Feature tf_idf = new Tf_IdfFeature(counts, docs.get(i));
Feature lda = new LDAFeature(inferredProbs, docs.get(i));
s[i] =
new FeatureBasedSummary(docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH, probs[i], tf_idf, lda);
}
summarizers.add(s);
s = new Summerizer[docs.size()];
for (int i = 0; i < s.length; i++) {
System.out.println("Generating FeatureBased_Sent #" + (i + 1));
Feature tf_idf = new Tf_IdfFeature(counts, docs.get(i));
Feature lda = new LDAFeature(inferredProbs, docs.get(i));
s[i] =
new FeatureBasedSummary_Sent(
docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH, probs[i], tf_idf, lda);
}
summarizers.add(s);
s = new Summerizer[docs.size()];
for (int i = 0; i < s.length; i++) {
System.out.println("Generating FeatureBased_BagOfWords #" + (i + 1));
Feature tf_idf = new Tf_IdfFeature(counts, docs.get(i));
Feature lda = new LDAFeature(inferredProbs, docs.get(i));
s[i] =
new FeatureBasedSummary_BagOfWords(
docs.get(i), DEFAULT_MAX_SUMMARY_LENGTH, probs[i], tf_idf, lda);
}
summarizers.add(s);
// s = new Summerizer[docs.size()];
// for (int i = 0; i < s.length; i++) {
// System.out.println("Generating MostProbSentSimpleGreedy #" + (i+1));
// s[i] = new MostProbSentSimpleGreedy(docs.get(i),
// DEFAULT_MAX_SUMMARY_LENGTH, probs[i]);
// }
// summarizers.add(s);
return summarizers;
}
/**
* @param args
* @throws IOException
*/
public static void main(String[] args) throws IOException {
// Load config files
System.err.println("Loading config files");
Config conf = ConfigFactory.loadConfiguration(Config.class, Config.DEFAULT);
IOConfig ioConf = ConfigFactory.loadConfiguration(IOConfig.class, IOConfig.DEFAULT);
LDAEstimatorConfig estConf =
ConfigFactory.loadConfiguration(LDAEstimatorConfig.class, LDAEstimatorConfig.DEFAULT);
LDAInferenceConfig infConf =
ConfigFactory.loadConfiguration(LDAInferenceConfig.class, LDAInferenceConfig.DEFAULT);
main m = new main(conf, ioConf);
// Load files that we want to summarize
System.err.println("Loading documents");
m.loadFiles();
// for (CoreMap sentence : m.documents.get(1).getAno()
// .get(SentencesAnnotation.class)) {
// Tree tree = sentence.get(TreeAnnotation.class);
// tree.pennPrint();
// }
// for (Doc d : m.documents) {
// for (CoreLabel token : d.getAno().get(TokensAnnotation.class)) {
// System.out.println("Token (NE): "
// + token.get(TextAnnotation.class) + " ("
// + token.get(NamedEntityTagAnnotation.class) + ")");
// }
// }
// Load topic models
System.err.println("Loading topic models");
LDAProbs inferredModel = LDAProbsLoader.loadLDAProbs(estConf, infConf);
// Loading clusters
System.err.println("Loading doc clusters");
DocCluster trainCluster = SerializableWrapper.readObject(DocCluster.CLUSTER_100_PATH);
// Assign docs to clusters
System.err.println("Assigning docs to clusters");
List<Integer> clusterAssign = m.assignDocClusters(inferredModel);
// Get a list of ngram probabilities for each document
System.err.println("Getting doc ngram probabilities");
NGramProbs[] probs = m.genDocNGramProbs(clusterAssign, trainCluster);
// System.err.println("Generating list of summarizers");
List<Summerizer[]> summarizers = m.generateSummarizerList(m.documents, probs, inferredModel);
// List<Summerizer[]> summarizers =
// m.generateSummarizerList(m.documents,
// null, inferredModel);
Doc[][] summaries = new Doc[summarizers.size()][m.documents.size()];
for (int i = 0; i < m.documents.size(); i++) {
for (int j = 0; j < summarizers.size(); j++) {
try {
System.out.println("Generating summary (" + j + ", " + i + ")");
m.generateSummary(m.documents.get(i), summarizers.get(j)[i]);
// System.out.println(m.documents.get(i).summary);
summaries[j][i] = new Doc();
summaries[j][i].f = m.documents.get(i).f;
summaries[j][i].summary = m.documents.get(i).summary;
} catch (Exception e) {
e.printStackTrace();
summaries[j][i] = new Doc();
summaries[j][i].f = m.documents.get(i).f;
summaries[j][i].summary = "NO_SUM";
continue;
}
}
m.documents.get(i).setAno(null);
}
System.out.println("Start calcuating ROUGE");
int count = 0;
// Generate the ROUGE evaluation file
String rougeInFile = "ROUGE-IN.xml";
RougeEvalBuilder reb = m.genRouge();
reb.write(rougeInFile);
FileWriter fw = new FileWriter(new File("summariesCollapsed"));
for (Doc[] docSums : summaries) {
fw.write(summarizers.get(count)[0].getClass() + "\n");
for (Doc doc : docSums) {
fw.write(doc.summary.replaceAll("\n", " ") + "\n");
}
// Write the summaries to disk
m.writeSummaries(docSums, summarizers.get(count)[0].getClass());
// Run the ROUGE script on the generated summaries and print the
// results
RougeScript rs = new RougeScript(conf.getRougePath(), 95, 500, 2, 1.2);
System.out.println("Writing summaries to: results-" + summarizers.get(count)[0].getClass());
rs.run(rougeInFile, "results-" + summarizers.get(count++)[0].getClass());
// RougeResults results = rs.run(rougeInFile);
// System.out.println(summarizers.get(count++)[0].getClass());
// System.out.println(results.getNgramAvgF(1));
}
fw.close();
// for (Summerizer[] s : summarizers) {
// System.err.println("Generating summaries for " + s.getClass());
// // Generate summaries
// for (int i = 0; i < s.length; i++) {
// m.generateSummary(m.documents.get(i), s[i]);
// System.out.println(m.documents.get(i).summary);
//
// // Reset annotation to null so that we don't run out of memory
// m.documents.get(i).setAno(null);
// }
//
// // Write the summaries to disk
// m.writeSummaries();
//
// // Generate the ROUGE evaluation file
// String rougeInFile = "ROUGE-IN.xml";
// RougeEvalBuilder reb = m.genRouge();
// reb.write(rougeInFile);
//
// // Run the ROUGE script on the generated summaries and print the
// // results
// RougeScript rs = new RougeScript(conf.getRougePath(), 95, 500, 2,
// 1.2);
// RougeResults results = rs.run(rougeInFile);
// System.out.println(s[0].getClass());
// System.out.println(results.getNgramAvgF(1));
//
// }
}