prototype summarization

.gitignore

@@ -1,6 +1,7 @@
 # misc.
 graph.png
 venv/
+nohup.out
 
 # docs -- in progress
 docs/

README.md

@@ -111,7 +111,7 @@ text = "Compatibility of systems of linear constraints over the set of natural n
 # load a spaCy model, depending on language, scale, etc.
 nlp = spacy.load("en_core_web_sm")
 
-# add PyTextRank to the spaCy pipline
+# add PyTextRank to the spaCy pipeline
 tr = pytextrank.TextRank()
 nlp.add_pipe(tr.PipelineComponent, name="textrank", last=True)
 

example.py

@@ -24,7 +24,7 @@
 logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
 logger = logging.getLogger("PyTR")
 
-# add PyTextRank into the spaCy pipline
+# add PyTextRank into the spaCy pipeline
 
 tr = pytextrank.TextRank(logger=None)
 nlp.add_pipe(tr.PipelineComponent, name="textrank", last=True)

explain_algo.ipynb

@@ -4,11 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Graph Construction\n",
+    "# Explain PyTextRank: the algorithm\n",
     "\n",
-    "Let's look at how to build a graph from a raw text.\n",
-    "\n",
-    "This work is based on \n",
+    "Let's look at the *TextRank* algorithm used to build a graph from a raw text, and then from that extract the top-ranked phrases. This work is based on \n",
     "[\"TextRank: Bringing Order into Text\"](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf),\n",
     "Rada Mihalcea, Paul Tarau, _Empirical Methods in Natural Language Processing_ (2004)."
    ]
@@ -18,7 +16,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    "First we perform some basic housekeeping for Jupyter and then load `spaCy` ..."
+    "First we perform some basic housekeeping for Jupyter, then load `spaCy` with a language model for English ..."
    ]
   },
   {