summpy/lexrank.py
def lexrank(sentences, continuous=False, sim_threshold=0.1, alpha=0.9,
            use_divrank=False, divrank_alpha=0.25):
    '''
    compute centrality score of sentences.

    Args:
      sentences: [u'こんにちは．', u'私の名前は飯沼です．', ... ]
      continuous: if True, apply continuous LexRank. (see reference)
      sim_threshold: if continuous is False and smilarity is greater or
        equal to sim_threshold, link the sentences.
      alpha: the damping factor of PageRank and DivRank
      divrank: if True, apply DivRank instead of PageRank
      divrank_alpha: strength of self-link [0.0-1.0]
        (it's not the damping factor, see divrank.py)

    Returns: tuple
      (
        {
          # sentence index -> score
          0: 0.003,
          1: 0.002,
          ...
        },
        similarity_matrix
      )
    
    Reference:
      Günes Erkan and Dragomir R. Radev.
      LexRank: graph-based lexical centrality as salience in text
      summarization. (section 3)
      http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html
    '''
    # configure ranker
    ranker_params = {'max_iter': 1000}
    if use_divrank:
        ranker = divrank_scipy
        ranker_params['alpha'] = divrank_alpha
        ranker_params['d'] = alpha
    else:
        ranker = networkx.pagerank_scipy
        ranker_params['alpha'] = alpha

    graph = networkx.DiGraph()

    # sentence -> tf
    sent_tf_list = []
    for sent in sentences:
        words = tools.word_segmenter_ja(sent)
        tf = collections.Counter(words)
        sent_tf_list.append(tf)

    sent_vectorizer = DictVectorizer(sparse=True)
    sent_vecs = sent_vectorizer.fit_transform(sent_tf_list)

    # compute similarities between senteces
    sim_mat = 1 - pairwise_distances(sent_vecs, sent_vecs, metric='cosine')

    if continuous:
        linked_rows, linked_cols = numpy.where(sim_mat > 0)
    else:
        linked_rows, linked_cols = numpy.where(sim_mat >= sim_threshold)

    # create similarity graph
    graph.add_nodes_from(range(sent_vecs.shape[0]))
    for i, j in zip(linked_rows, linked_cols):
        if i == j:
            continue
        weight = sim_mat[i,j] if continuous else 1.0
        graph.add_edge(i, j, {'weight': weight})

    scores = ranker(graph, **ranker_params)
    return scores, sim_mat
def lexrank(sentences, continuous=False, sim_threshold=0.1, alpha=0.9,
            use_divrank=False, divrank_alpha=0.25):
    '''
    compute centrality score of sentences.

    Args:
      sentences: [u'こんにちは．', u'私の名前は飯沼です．', ... ]
      continuous: if True, apply continuous LexRank. (see reference)
      sim_threshold: if continuous is False and smilarity is greater or
        equal to sim_threshold, link the sentences.
      alpha: the damping factor of PageRank and DivRank
      divrank: if True, apply DivRank instead of PageRank
      divrank_alpha: strength of self-link [0.0-1.0]
        (it's not the damping factor, see divrank.py)

    Returns: tuple
      (
        {
          # sentence index -> score
          0: 0.003,
          1: 0.002,
          ...
        },
        similarity_matrix
      )
    
    Reference:
      Günes Erkan and Dragomir R. Radev.
      LexRank: graph-based lexical centrality as salience in text
      summarization. (section 3)
      http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html
    '''
    # configure ranker
    ranker_params = {'max_iter': 1000}
    if use_divrank:
        ranker = divrank_scipy
        ranker_params['alpha'] = divrank_alpha
        ranker_params['d'] = alpha
    else:
        ranker = networkx.pagerank_scipy
        ranker_params['alpha'] = alpha

    graph = networkx.DiGraph()

    # sentence -> tf
    sent_tf_list = []
    for sent in sentences:
        words = tools.word_segmenter_ja(sent)
        tf = collections.Counter(words)
        sent_tf_list.append(tf)

    sent_vectorizer = DictVectorizer(sparse=True)
    sent_vecs = sent_vectorizer.fit_transform(sent_tf_list)

    # compute similarities between senteces
    sim_mat = 1 - pairwise_distances(sent_vecs, sent_vecs, metric='cosine')

    if continuous:
        linked_rows, linked_cols = numpy.where(sim_mat > 0)
    else:
        linked_rows, linked_cols = numpy.where(sim_mat >= sim_threshold)

    # create similarity graph
    graph.add_nodes_from(range(sent_vecs.shape[0]))
    for i, j in zip(linked_rows, linked_cols):
        if i == j:
            continue
        weight = sim_mat[i,j] if continuous else 1.0
        graph.add_edge(i, j, {'weight': weight})

    scores = ranker(graph, **ranker_params)
    return scores, sim_mat
compute centrality score of sentences.

    Args:
      sentences: [u'こんにちは．', u'私の名前は飯沼です．', ... ]
      continuous: if True, apply continuous LexRank. (see reference)
      sim_threshold: if continuous is False and smilarity is greater or
        equal to sim_threshold, link the sentences.
      alpha: the damping factor of PageRank and DivRank
      divrank: if True, apply DivRank instead of PageRank
      divrank_alpha: strength of self-link [0.0-1.0]
        (it's not the damping factor, see divrank.py)

    Returns: tuple
      (
        {
          # sentence index -> score
          0: 0.003,
          1: 0.002,
          ...
        },
        similarity_matrix
      )
    
    Reference:
      Günes Erkan and Dragomir R. Radev.
      LexRank: graph-based lexical centrality as salience in text
      summarization. (section 3)
      http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html