document_sum.py

import json
import nltk
import numpy
import page_rank
import rake_algorithm

BLOG_DATA = 'feed.json'

N = 100 # Number of words to consider
CLUSTER_THRESHOLD = 5 # Distance between words to consider
TOP_SENTENCES = 5 # Number of sentences to return for a "top n" summary

# Approach taken from "The Automatic Creation of Literature Abstracts" by H.P Luhn

def _score_sentences(sentences, important_words):
    scores = []
    sentence_idx = -1

    for s in [nltk.tokenize.word_tokenize(s) for s in sentences]:
        sentence_idx += 1
        word_idx = []

        # For each word in the word list...
        for w in important_words:
            try:
                # Compute an index for where any important words occur in the sentence.
                word_idx.append(s.index(w))
            except ValueError, e: # w not in this particular sentence
                pass

        word_idx.sort()

        # It is possible that some sentences may not contain any important words at all.
        if len(word_idx) == 0: continue

        # Using the word index, compute clusters by using max distance threshold
        # for any two consecutive words.

        clusters = []
        cluster = [word_idx[0]]
        i = 1
        while i < len(word_idx):
            if word_idx[i] - word_idx[i - 1] < CLUSTER_THRESHOLD:
                cluster.append(word_idx[i])
            else:
                clusters.append(cluster[:])
                cluster = [word_idx[i]]
            i += 1
        clusters.append(cluster)

        # Score each cluster. The max score for any given cluster is the score
        # for the sentence.

        max_cluster_score = 0
        for c in clusters:
            significant_words_in_cluster = len(c)
            total_words_in_cluster = c[-1] - c[0] + 1
            score = 1.0 * significant_words_in_cluster \
                    * significant_words_in_cluster / total_words_in_cluster

            if score > max_cluster_score:
                max_cluster_score = score

        scores.append((sentence_idx, score))

    return scores

def summarize(txt):
    sentences = [s for s in nltk.tokenize.sent_tokenize(txt)]
    normalized_sentences = [s.lower() for s in sentences]
    words = [w.lower() for sentence in normalized_sentences for w in
            nltk.tokenize.word_tokenize(sentence)]
    fdist = nltk.FreqDist(words)

    top_n_words = [w[0] for w in fdist.items()
            if w[0] not in nltk.corpus.stopwords.words('english')][:N]
    scored_sentences = _score_sentences(normalized_sentences, top_n_words)

    # Summarization Approach 1:
    # Filter out nonsignificant sentences by using the average score plus a
    # fraction of the std dev as a filter

    avg = numpy.mean([s[1] for s in scored_sentences])
    std = numpy.std([s[1] for s in scored_sentences])
    mean_scored = [(sent_idx, score) for (sent_idx, score) in scored_sentences
            if score > avg + 0.5 * std]

    # Summarization Approach 2:
    # Another approach would be to return only the top N ranked sentences
    top_n_scored = sorted(scored_sentences, key=lambda s: s[1])[-TOP_SENTENCES:]
    top_n_scored = sorted(top_n_scored, key=lambda s: s[0])

    # Decorate the post object with summaries
    rake = rake_algorithm.RakeKeywordExtractor()
    return dict(top_n_summary=[sentences[idx] for (idx, score) in top_n_scored],
            mean_scored_summary=[sentences[idx] for (idx, score) in mean_scored],
            page_rank=page_rank.summarize(txt),
            rake_algorithm=rake.extract(txt))

blog_data = json.loads(open(BLOG_DATA).read())

for post in blog_data:
    post.update(summarize(post['content']))

    print post['title']
    print '=' * len(post['title'])
    print
    print 'Top N Summary'
    print '------------------------'
    print ' '.join(post['top_n_summary'])
    print
    print 'Mean Scored Summary'
    print '------------------------'
    print ' '.join(post['mean_scored_summary'])
    print
    print 'Page Rank Summary'
    print '------------------------'
    print ' '.join(post['page_rank'])
    print