[WIP] Refactor documentation API Reference for gensim.summarization

gensim/summarization/commons.py

@@ -3,14 +3,14 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
-"""This module provides functions of creatinf graph from sequence of values and
+"""This module provides functions of creating graph from sequence of values and
 removing of unreachable nodes.
 
 
 Examples
 --------
 
-Create simple graph and add edges. Let's kake a look at nodes.
+Create simple graph and add edges. Let's take a look at nodes.
 
 >>> gg = build_graph(['Felidae', 'Lion', 'Tiger', 'Wolf'])
 >>> gg.add_edge(("Felidae", "Lion"))

gensim/summarization/keywords.py

@@ -199,6 +199,36 @@ def _format_results(_keywords, combined_keywords, split, scores):
 
 def keywords(text, ratio=0.2, words=None, split=False, scores=False, pos_filter=('NN', 'JJ'),
              lemmatize=False, deacc=True):
+    """.
+
+    Parameters
+    ----------
+    text : str
+        Sequence of values.
+    ratio : float
+        If no "words" option is selected, the number of sentences is
+        reduced by the provided ratio, else, the ratio is ignored.
+    words : list
+        .
+    split : bool
+        .
+    scores : bool
+        .
+    pos_filter : tuple
+        Part of speech filters.
+    lemmatize : bool
+        Lemmatize words, optional.
+    deacc : bool
+        Remove accentuation, optional.
+
+    Returns
+    -------
+    Graph
+        Created graph.
+
+    """
+
+
     # Gets a dict of word -> lemma
     text = to_unicode(text)
     tokens = _clean_text_by_word(text, deacc=deacc)
@@ -233,6 +263,20 @@ def keywords(text, ratio=0.2, words=None, split=False, scores=False, pos_filter=
 
 
 def get_graph(text):
+    """Creates and returns graph with given text. Cleans, tokenizes text 
+    before creating a graph.
+
+    Parameters
+    ----------
+    text : str
+        Sequence of values.
+
+    Returns
+    -------
+    Graph
+        Created graph.
+
+    """
     tokens = _clean_text_by_word(text)
     split_text = list(_tokenize_by_word(text))