def generateSummaries(sentences, length=100, mode = "Extractive", ranker = rankingModes['TR']):
'''
This is where the ILP works to select the best sentences and form the summary
'''
if mode == "Abstractive":
import kenlm
lm = kenlm.LanguageModel(RESOURCES_DIR+'/lm-3g.klm')
'''
Here sentences should have POS tagged format
'''
taggedsentences=[]
for sent in sentences:
sent=sent.decode('utf-8','ignore')
tagged_sent=''
tagged_tokens=nltk.pos_tag(nltk.word_tokenize(sent))
for token in tagged_tokens:
word, pos=token
tagged_sent=tagged_sent+' '+word+"/"+pos
taggedsentences.append(tagged_sent.strip())
sentences=bigramTweetGenerator(taggedsentences)
genSentences, svolist=wg.retrieveNewSentences(sentences, stopwords)
if len(genSentences) <= 1:
return [k for k, v in genSentences]
finalSentencesRetained=wg.solveILPFactBased(genSentences,
lm,
stopwords,
ranker,
intraGenSimThreshold=0.5,
l_max=length,
mode="Abstractive"
)
summary=txtFromSents(finalSentencesRetained)
print "=======Summary:===== \n", summary
if mode == "Extractive":
lm=[] #No need of language model in Extractive
#if len(sentences) <= 2:
# summary=txtFromSents(sentences)
# print "Summary: ", summary
# return
print sentences
finalSentencesRetained=wg.solveILPFactBased(sentences,
lm,
stopwords,
ranker,
intraGenSimThreshold=0.7,
l_max=length,
mode="Extractive"
)
print 'Final sentences,', finalSentencesRetained
summary=txtFromSents(finalSentencesRetained)
print "=======Summary:===== \n", summary
def generateSummaries(sentences,
length=100,
mode="Extractive",
ranker=rankingModes['TR']):
'''
This is where the ILP works to select the best sentences and form the summary
'''
if mode == "Abstractive":
import kenlm
lm = kenlm.LanguageModel(RESOURCES_DIR + '/lm-3g.klm')
'''
Here sentences should have POS tagged format
'''
taggedsentences = []
for sent in sentences:
sent = sent.decode('utf-8', 'ignore')
tagged_sent = ''
tagged_tokens = nltk.pos_tag(nltk.word_tokenize(sent))
for token in tagged_tokens:
word, pos = token
tagged_sent = tagged_sent + ' ' + word + "/" + pos
taggedsentences.append(tagged_sent.strip())
sentences = bigramTweetGenerator(taggedsentences)
genSentences, svolist = wg.retrieveNewSentences(sentences, stopwords)
if len(genSentences) <= 1:
return [k for k, v in genSentences]
finalSentencesRetained = wg.solveILPFactBased(genSentences,
lm,
stopwords,
ranker,
intraGenSimThreshold=0.5,
l_max=length,
mode="Abstractive")
summary = txtFromSents(finalSentencesRetained)
print "=======Summary:===== \n", summary
if mode == "Extractive":
lm = [] #No need of language model in Extractive
#if len(sentences) <= 2:
# summary=txtFromSents(sentences)
# print "Summary: ", summary
# return
print sentences
finalSentencesRetained = wg.solveILPFactBased(sentences,
lm,
stopwords,
ranker,
intraGenSimThreshold=0.7,
l_max=length,
mode="Extractive")
print 'Final sentences,', finalSentencesRetained
summary = txtFromSents(finalSentencesRetained)
print "=======Summary:===== \n", summary
import absummarizer.WGGraph as wg
import os
import re
import nltk
from absummarizer.summarizer import segmentize
from flask import Flask, render_template, flash, request
from wtforms import Form, TextField, TextAreaField, validators, StringField, SubmitField
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
PROJECT_DIR=os.path.dirname(__file__)+"./"
print ("Project dir", PROJECT_DIR)
RESOURCES_DIR=PROJECT_DIR+"resources/"
stopwords=wg.load_stopwords("resources/stopwords.en.dat")
rankingModes={"C":"Centroid","TR":"textrank", "CW":"contentWeighing"}
def sentenceCapitalize(sent):
sentences = sent.split(". ")
sentences2 = [sentence[0].capitalize() + sentence[1:] for sentence in sentences]
string2 = '. '.join(sentences2)
return string2
def tweetCleaner(sentences):
p=re.compile(r'http?:\/\/.*[\s\r\n]*', re.DOTALL) #Regex to remove http from sentences
p2=re.compile(r'(^|\s)#.+?\s', re.DOTALL) #Regex
p3=re.compile(r'(^|\s)@.+?(\s|$)', re.DOTALL)
print ("Initial sentences=>", len(sentences))
final_sentences=[]
# -*- coding: utf-8 -*-
'''
Created on Aug 21, 2015
@author: siddban
'''
import absummarizer.WGGraph as wg
import os
import re
import nltk
from absummarizer.summarizer import segmentize
PROJECT_DIR = os.path.dirname(__file__) + "/../"
print "Project dir", PROJECT_DIR
RESOURCES_DIR = "resources/"
stopwords = wg.load_stopwords(RESOURCES_DIR + "stopwords.en.dat")
rankingModes = {"C": "Centroid", "TR": "textrank", "CW": "contentWeighing"}
def sentenceCapitalize(sent):
sentences = sent.split(". ")
sentences2 = [
sentence[0].capitalize() + sentence[1:] for sentence in sentences
]
string2 = '. '.join(sentences2)
return string2
def tweetCleaner(sentences):
p = re.compile(r'http?:\/\/.*[\s\r\n]*',
# -*- coding: utf-8 -*-
'''
Created on Aug 21, 2015
@author: siddban
'''
import absummarizer.WGGraph as wg
import os
import re
import nltk
from absummarizer.summarizer import segmentize
PROJECT_DIR=os.path.dirname(__file__)+"/../"
print "Project dir", PROJECT_DIR
RESOURCES_DIR="resources/"
stopwords=wg.load_stopwords(RESOURCES_DIR+"stopwords.en.dat")
rankingModes={"C":"Centroid","TR":"textrank", "CW":"contentWeighing"}
def sentenceCapitalize(sent):
sentences = sent.split(". ")
sentences2 = [sentence[0].capitalize() + sentence[1:] for sentence in sentences]
string2 = '. '.join(sentences2)
return string2
def tweetCleaner(sentences):
p=re.compile(r'http?:\/\/.*[\s\r\n]*', re.DOTALL) #Regex to remove http from sentences
p2=re.compile(r'(^|\s)#.+?\s', re.DOTALL) #Regex
p3=re.compile(r'(^|\s)@.+?(\s|$)', re.DOTALL)
print "Initial sentences=>", len(sentences)
final_sentences=[]