originalankur

import sys

import contextualSpellCheck
import spacy

def filter_words(text):
    text = text.strip()
    if text in nlp.Defaults.stop_words:
        print("in stop word", text)
        return False

originalankur

<script src="https://cdn.safetycop.io/bundle.embed.1982642.min.js" crossorigin="anonymous"></script>

originalankur

"""feedfinder: Find the Web feed for a Web page
http://www.aaronsw.com/2002/feedfinder/

Usage:
  feed(uri) - returns feed found for a URI
  feeds(uri) - returns all feeds found for a URI

    >>> import feedfinder
    >>> feedfinder.feed('scripting.com')
    'http://scripting.com/rss.xml'

originalankur

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""

pip install networkx distance pattern 

In Flipboard's article[1], they kindly divulge their interpretation
of the summarization technique called LexRank[2].

originalankur

def normalize_line(line):
    return [piece.strip() for piece in line.split("|")[1:-1]]

def is_valid_line(line):
    return "|" in line

def load(text):
    lines =  map(normalize_line,
                filter(is_valid_line,
                        text.strip().splitlines()))