text_summarizer_without_attention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4c3c193",
   "metadata": {
    "id": "c4c3c193"
   },
   "source": [
    "## Import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb3b7110",
   "metadata": {
    "id": "fb3b7110"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import string\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66027a19",
   "metadata": {
    "id": "66027a19"
   },
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e5a505e",
   "metadata": {
    "id": "9e5a505e"
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(r\"news_summary.csv\",encoding='iso-8859-1',usecols=['headlines','text'])\n",
    "df2 = pd.read_csv(r\"news_summary_more.csv\",encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "413d6ec2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "413d6ec2",
    "outputId": "cc6ef570-180f-4e1d-a92f-e3a893763a83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Daman &amp; Diu revokes mandatory Rakshabandhan in...</td>\n",
       "      <td>The Administration of Union Territory Daman an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Malaika slams user who trolled her for 'divorc...</td>\n",
       "      <td>Malaika Arora slammed an Instagram user who tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'Virgin' now corrected to 'Unmarried' in IGIMS...</td>\n",
       "      <td>The Indira Gandhi Institute of Medical Science...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaj aapne pakad liya: LeT man Dujana before be...</td>\n",
       "      <td>Lashkar-e-Taiba's Kashmir commander Abu Dujana...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hotel staff to get training to spot signs of s...</td>\n",
       "      <td>Hotels in Maharashtra will train their staff t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines  \\\n",
       "0  Daman & Diu revokes mandatory Rakshabandhan in...   \n",
       "1  Malaika slams user who trolled her for 'divorc...   \n",
       "2  'Virgin' now corrected to 'Unmarried' in IGIMS...   \n",
       "3  Aaj aapne pakad liya: LeT man Dujana before be...   \n",
       "4  Hotel staff to get training to spot signs of s...   \n",
       "\n",
       "                                                text  \n",
       "0  The Administration of Union Territory Daman an...  \n",
       "1  Malaika Arora slammed an Instagram user who tr...  \n",
       "2  The Indira Gandhi Institute of Medical Science...  \n",
       "3  Lashkar-e-Taiba's Kashmir commander Abu Dujana...  \n",
       "4  Hotels in Maharashtra will train their staff t...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44443515",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "44443515",
    "outputId": "72f8df8a-f17a-4c84-fbb2-bd61a7f7bdc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4514, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00b1b126",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "00b1b126",
    "outputId": "2cff56ce-977a-4749-efa4-41df28290947"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>upGrad learner switches to career in ML &amp; Al w...</td>\n",
       "      <td>Saurav Kant, an alumnus of upGrad and IIIT-B's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi techie wins free food from Swiggy for on...</td>\n",
       "      <td>Kunal Shah's credit card bill payment platform...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Zealand end Rohit Sharma-led India's 12-ma...</td>\n",
       "      <td>New Zealand defeated India by 8 wickets in the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aegon life iTerm insurance plan helps customer...</td>\n",
       "      <td>With Aegon Life iTerm Insurance plan, customer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Have known Hirani for yrs, what if MeToo claim...</td>\n",
       "      <td>Speaking about the sexual harassment allegatio...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines  \\\n",
       "0  upGrad learner switches to career in ML & Al w...   \n",
       "1  Delhi techie wins free food from Swiggy for on...   \n",
       "2  New Zealand end Rohit Sharma-led India's 12-ma...   \n",
       "3  Aegon life iTerm insurance plan helps customer...   \n",
       "4  Have known Hirani for yrs, what if MeToo claim...   \n",
       "\n",
       "                                                text  \n",
       "0  Saurav Kant, an alumnus of upGrad and IIIT-B's...  \n",
       "1  Kunal Shah's credit card bill payment platform...  \n",
       "2  New Zealand defeated India by 8 wickets in the...  \n",
       "3  With Aegon Life iTerm Insurance plan, customer...  \n",
       "4  Speaking about the sexual harassment allegatio...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdfbacd0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cdfbacd0",
    "outputId": "ba631430-2edc-47ce-8edd-4d5f8335aa49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98401, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efd6c87",
   "metadata": {
    "id": "2efd6c87"
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3815a7",
   "metadata": {
    "id": "2c3815a7"
   },
   "source": [
    "The aim of the preprocessing step is to reduce the\n",
    "dimensionality of the representation space, and it normally includes: (i) stop-word\n",
    "elimination – common words with no semantics and which do not aggregate relevant\n",
    "information to the task (e.g., “the”, “a”) are eliminated;; (ii) case folding: consists of\n",
    "converting all the characters to the same kind of letter case - either upper case or\n",
    "lower case; (iii) stemming: syntactically-similar words, such as plurals, verbal\n",
    "variations, etc. are considered similar; the purpose of this procedure is to obtain the\n",
    "stem or radix of each word, which emphasize its semantics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7ae0028",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "a7ae0028",
    "outputId": "7d2051ca-b1a5-46eb-a82b-1f880fa223ec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Daman &amp; Diu revokes mandatory Rakshabandhan in...</td>\n",
       "      <td>The Administration of Union Territory Daman an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Malaika slams user who trolled her for 'divorc...</td>\n",
       "      <td>Malaika Arora slammed an Instagram user who tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'Virgin' now corrected to 'Unmarried' in IGIMS...</td>\n",
       "      <td>The Indira Gandhi Institute of Medical Science...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaj aapne pakad liya: LeT man Dujana before be...</td>\n",
       "      <td>Lashkar-e-Taiba's Kashmir commander Abu Dujana...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hotel staff to get training to spot signs of s...</td>\n",
       "      <td>Hotels in Maharashtra will train their staff t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines  \\\n",
       "0  Daman & Diu revokes mandatory Rakshabandhan in...   \n",
       "1  Malaika slams user who trolled her for 'divorc...   \n",
       "2  'Virgin' now corrected to 'Unmarried' in IGIMS...   \n",
       "3  Aaj aapne pakad liya: LeT man Dujana before be...   \n",
       "4  Hotel staff to get training to spot signs of s...   \n",
       "\n",
       "                                                text  \n",
       "0  The Administration of Union Territory Daman an...  \n",
       "1  Malaika Arora slammed an Instagram user who tr...  \n",
       "2  The Indira Gandhi Institute of Medical Science...  \n",
       "3  Lashkar-e-Taiba's Kashmir commander Abu Dujana...  \n",
       "4  Hotels in Maharashtra will train their staff t...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate two dataframes to get total data\n",
    "df = pd.concat([df1,df2],axis=0,ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d71d0170",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d71d0170",
    "outputId": "50cb2089-592e-4e49-c07b-6253319a01cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102915, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8324b2c6",
   "metadata": {
    "id": "8324b2c6"
   },
   "outputs": [],
   "source": [
    "# drop duplicates and null values\n",
    "df.dropna(inplace=True)\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1c8949d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f1c8949d",
    "outputId": "cbd2b2d4-29a0-494c-e063-7bad83291321"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100266, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b4c656a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1b4c656a",
    "outputId": "3a085367-f0fc-48de-87d2-b68f17afac03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size : 100266\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size : {df.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13cf5a98",
   "metadata": {
    "id": "13cf5a98"
   },
   "outputs": [],
   "source": [
    "def cleantext(text):\n",
    "    text = str(text)\n",
    "    text=text.split()\n",
    "    words=[]\n",
    "    for t in text:\n",
    "        if t.isalpha():\n",
    "            words.append(t)\n",
    "    text=\" \".join(words)\n",
    "    text=text.lower()\n",
    "    text=re.sub(r\"what's\",\"what is \",text)\n",
    "    text=re.sub(r\"it's\",\"it is \",text)\n",
    "    text=re.sub(r\"\\'ve\",\" have \",text)\n",
    "    text=re.sub(r\"i'm\",\"i am \",text)\n",
    "    text=re.sub(r\"\\'re\",\" are \",text)\n",
    "    text=re.sub(r\"n't\",\" not \",text)\n",
    "    text=re.sub(r\"\\'d\",\" would \",text)\n",
    "    text=re.sub(r\"\\'s\",\"s\",text)\n",
    "    text=re.sub(r\"\\'ll\",\" will \",text)\n",
    "    text=re.sub(r\"can't\",\" cannot \",text)\n",
    "    text=re.sub(r\" e g \",\" eg \",text)\n",
    "    text=re.sub(r\"e-mail\",\"email\",text)\n",
    "    text=re.sub(r\"9\\\\/11\",\" 911 \",text)\n",
    "    text=re.sub(r\" u.s\",\" american \",text)\n",
    "    text=re.sub(r\" u.n\",\" united nations \",text)\n",
    "    text=re.sub(r\"\\n\",\" \",text)\n",
    "    text=re.sub(r\":\",\" \",text)\n",
    "    text=re.sub(r\"-\",\" \",text)\n",
    "    text=re.sub(r\"\\_\",\" \",text)\n",
    "    text=re.sub(r\"\\d+\",\" \",text)\n",
    "    text=re.sub(r\"[$#@%&*!~?%{}()]\",\" \",text)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d96dbc9",
   "metadata": {
    "id": "2d96dbc9"
   },
   "outputs": [],
   "source": [
    "for col in df.columns:\n",
    "    df[col] = df[col].apply(lambda x: cleantext(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be8f477a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "be8f477a",
    "outputId": "779fa9c7-e2b9-4927-aec6-5696a0bc6093"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daman diu revokes mandatory rakshabandhan in o...</td>\n",
       "      <td>the administration of union territory daman an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>malaika slams user who trolled her for rich</td>\n",
       "      <td>malaika arora slammed an instagram user who tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>now corrected to in form</td>\n",
       "      <td>the indira gandhi institute of medical science...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aaj aapne pakad let man dujana before being ki...</td>\n",
       "      <td>kashmir commander abu who was killed by securi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hotel staff to get training to spot signs of s...</td>\n",
       "      <td>hotels in maharashtra will train their staff t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines  \\\n",
       "0  daman diu revokes mandatory rakshabandhan in o...   \n",
       "1        malaika slams user who trolled her for rich   \n",
       "2                           now corrected to in form   \n",
       "3  aaj aapne pakad let man dujana before being ki...   \n",
       "4  hotel staff to get training to spot signs of s...   \n",
       "\n",
       "                                                text  \n",
       "0  the administration of union territory daman an...  \n",
       "1  malaika arora slammed an instagram user who tr...  \n",
       "2  the indira gandhi institute of medical science...  \n",
       "3  kashmir commander abu who was killed by securi...  \n",
       "4  hotels in maharashtra will train their staff t...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "928db258",
   "metadata": {
    "id": "928db258"
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame()\n",
    "data['text'] = df['text']\n",
    "data['summary'] = df['headlines']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7c8330",
   "metadata": {
    "id": "4f7c8330"
   },
   "source": [
    "Remember to add the START and END special tokens at the beginning and end of the summary. Here, I have chosen sostok and eostok as START and END tokens. The beginning-of-sentence token serves as an instruction to the decoder to start decoding (it needs a very first state to predict what the next first token is). The end-of-sentence token is an instruction for the decoding algorithm to stop generating more tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177724a5",
   "metadata": {
    "id": "177724a5"
   },
   "source": [
    "## Understanding distribution of sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c09d22",
   "metadata": {
    "id": "d9c09d22"
   },
   "source": [
    "Here, we will analyze the length of the reviews and the summary to get an overall idea about the distribution of length of the text. This will help us fix the maximum length of the sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "230b60d4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "230b60d4",
    "outputId": "b391109d-1c80-4f50-9579-f4eaf4423996"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999900265294317\n"
     ]
    }
   ],
   "source": [
    "text_count = 0\n",
    "for text in data['text']:\n",
    "    if len(text.split())<60:\n",
    "        text_count += 1\n",
    "print(text_count/len(data['text']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3de49ace",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3de49ace",
    "outputId": "5bcb6983-77d9-4c0d-b9ac-605704e2bee1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999601061177269\n"
     ]
    }
   ],
   "source": [
    "summary_count = 0\n",
    "for summary in data['summary']:\n",
    "    if len(summary.split())<15:\n",
    "        summary_count+=1\n",
    "print(summary_count/len(data['summary']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f3126cac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "f3126cac",
    "outputId": "175fc66e-b38b-47b4-d02a-d573c16a130d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_word_count = []\n",
    "summary_word_count = []\n",
    "\n",
    "# get number of words in text and summary\n",
    "\n",
    "for i in data['text']:\n",
    "    text_word_count.append(len(i.split()))\n",
    "    \n",
    "for i in data['summary']:\n",
    "    summary_word_count.append(len(str(i).split()))\n",
    "    \n",
    "length = pd.DataFrame()\n",
    "length['text'] = pd.Series(text_word_count)\n",
    "length['summary'] = pd.Series(summary_word_count)\n",
    "\n",
    "length.hist(bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74bd91f9",
   "metadata": {
    "id": "74bd91f9"
   },
   "outputs": [],
   "source": [
    "max_text_len=60\n",
    "max_summary_len=17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7de50c82",
   "metadata": {
    "id": "7de50c82"
   },
   "outputs": [],
   "source": [
    "\n",
    "text =np.array(data['text'])\n",
    "summary=np.array(data['summary'])\n",
    "\n",
    "short_text=[]\n",
    "short_summary=[]\n",
    "\n",
    "for i in range(len(text)):\n",
    "    if(len(str(summary[i]).split())<=max_summary_len and len(text[i].split())<=max_text_len):\n",
    "        short_text.append(text[i])\n",
    "        short_summary.append(summary[i])\n",
    "        \n",
    "df=pd.DataFrame({'text':short_text,'summary':short_summary})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6YSU0ipQQqhD",
   "metadata": {
    "id": "6YSU0ipQQqhD"
   },
   "outputs": [],
   "source": [
    "df['summary'] = df['summary'].apply(lambda x : 'sostok '+ x + ' eostok')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04e5ed2",
   "metadata": {
    "id": "d04e5ed2"
   },
   "source": [
    "## Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8187055c",
   "metadata": {
    "id": "8187055c"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_tr,x_val,y_tr,y_val=train_test_split(np.array(df['text']),np.array(df['summary']),test_size=0.1,random_state=0,shuffle=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96521441",
   "metadata": {
    "id": "96521441"
   },
   "source": [
    "## Tokenize: text_to_word_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28735145",
   "metadata": {
    "id": "28735145"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer \n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "#prepare a tokenizer for reviews on training data\n",
    "x_tokenizer = Tokenizer() \n",
    "x_tokenizer.fit_on_texts(list(x_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26da2c75",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "26da2c75",
    "outputId": "9b899b17-9cd7-41dd-ad78-dd9a427294ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of rare words in vocabulary: 56.57998805546139\n",
      "Total Coverage of rare words: 1.1902033035582427\n"
     ]
    }
   ],
   "source": [
    "thresh=4\n",
    "\n",
    "cnt=0\n",
    "tot_cnt=0\n",
    "freq=0\n",
    "tot_freq=0\n",
    "\n",
    "for key,value in x_tokenizer.word_counts.items():\n",
    "    tot_cnt=tot_cnt+1\n",
    "    tot_freq=tot_freq+value\n",
    "    if(value<thresh):\n",
    "        cnt=cnt+1\n",
    "        freq=freq+value\n",
    "    \n",
    "print(\"% of rare words in vocabulary:\",(cnt/tot_cnt)*100)\n",
    "print(\"Total Coverage of rare words:\",(freq/tot_freq)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77112b0",
   "metadata": {
    "id": "e77112b0"
   },
   "source": [
    "## Padding: pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2a2cf8b3",
   "metadata": {
    "id": "2a2cf8b3"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "x_tokenizer = Tokenizer(num_words=tot_cnt-cnt) \n",
    "x_tokenizer.fit_on_texts(list(x_tr))\n",
    "\n",
    "#convert text sequences into integer sequences\n",
    "x_tr_seq    =   x_tokenizer.texts_to_sequences(x_tr) \n",
    "x_val_seq   =   x_tokenizer.texts_to_sequences(x_val)\n",
    "\n",
    "#padding zero upto maximum length\n",
    "x_tr    =   pad_sequences(x_tr_seq,  maxlen=max_text_len, padding='post')\n",
    "x_val   =   pad_sequences(x_val_seq, maxlen=max_text_len, padding='post')\n",
    "\n",
    "#size of vocabulary ( +1 for padding token)\n",
    "x_voc   =  x_tokenizer.num_words + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08fc31c1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "08fc31c1",
    "outputId": "b98299b8-5291-430b-855b-39b219d595d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26901"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_voc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e86bc86f",
   "metadata": {
    "id": "e86bc86f"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "y_tokenizer = Tokenizer()   \n",
    "y_tokenizer.fit_on_texts(list(y_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63703ff4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "63703ff4",
    "outputId": "7fbef540-c1b0-444d-a577-8da82c49b22d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of rare words in vocabulary: 55.537201701050854\n",
      "Total Coverage of rare words: 2.6054205341817265\n"
     ]
    }
   ],
   "source": [
    "thresh=4\n",
    "\n",
    "cnt=0\n",
    "tot_cnt=0\n",
    "freq=0\n",
    "tot_freq=0\n",
    "\n",
    "for key,value in y_tokenizer.word_counts.items():\n",
    "    tot_cnt=tot_cnt+1\n",
    "    tot_freq=tot_freq+value\n",
    "    if(value<thresh):\n",
    "        cnt=cnt+1\n",
    "        freq=freq+value\n",
    "    \n",
    "print(\"% of rare words in vocabulary:\",(cnt/tot_cnt)*100)\n",
    "print(\"Total Coverage of rare words:\",(freq/tot_freq)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c85184cc",
   "metadata": {
    "id": "c85184cc"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "y_tokenizer = Tokenizer(num_words=tot_cnt-cnt) \n",
    "y_tokenizer.fit_on_texts(list(y_tr))\n",
    "\n",
    "#convert text sequences into integer sequences\n",
    "y_tr_seq    =   y_tokenizer.texts_to_sequences(y_tr) \n",
    "y_val_seq   =   y_tokenizer.texts_to_sequences(y_val) \n",
    "\n",
    "#padding zero upto maximum length\n",
    "y_tr    =   pad_sequences(y_tr_seq, maxlen=max_summary_len, padding='post')\n",
    "y_val   =   pad_sequences(y_val_seq, maxlen=max_summary_len, padding='post')\n",
    "\n",
    "#size of vocabulary\n",
    "y_voc  =   y_tokenizer.num_words +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ac81b365",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ac81b365",
    "outputId": "0c742133-c117-4eda-9668-af08b54b41e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12652"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_voc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "622e86af",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "622e86af",
    "outputId": "ed4c8b63-593b-4901-e4d3-3405493fd33d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Conda\\envs\\directml\\lib\\site-packages\\tensorflow_core\\python\\keras\\initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From D:\\Conda\\envs\\directml\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 60)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 60, 100)      2690100     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     [(None, 60, 300), (N 481200      embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   [(None, 60, 300), (N 721200      lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, None, 100)    1265200     input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   [(None, 60, 300), (N 721200      lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   [(None, None, 300),  481200      embedding_1[0][0]                \n",
      "                                                                 lstm_2[0][1]                     \n",
      "                                                                 lstm_2[0][2]                     \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed (TimeDistribut (None, None, 12652)  3808252     lstm_3[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 10,168,352\n",
      "Trainable params: 10,168,352\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import backend as K\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Embedding,LSTM,Input,TimeDistributed,Dense\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "K.clear_session()\n",
    "\n",
    "K.clear_session()\n",
    "\n",
    "latent_dim = 300\n",
    "embedding_dim=100\n",
    "\n",
    "# Encoder\n",
    "encoder_inputs = Input(shape=(max_text_len,))\n",
    "\n",
    "#embedding layer\n",
    "enc_emb =  Embedding(x_voc, embedding_dim,trainable=True)(encoder_inputs)\n",
    "\n",
    "#encoder lstm 1\n",
    "encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4)\n",
    "encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb)\n",
    "\n",
    "#encoder lstm 2\n",
    "encoder_lstm2 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4)\n",
    "encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1)\n",
    "\n",
    "#encoder lstm 3\n",
    "encoder_lstm3=LSTM(latent_dim, return_state=True, return_sequences=True,dropout=0.4,recurrent_dropout=0.4)\n",
    "encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2)\n",
    "\n",
    "# Set up the decoder, using `encoder_states` as initial state.\n",
    "decoder_inputs = Input(shape=(None,))\n",
    "\n",
    "#embedding layer\n",
    "dec_emb_layer = Embedding(y_voc, embedding_dim,trainable=True)\n",
    "dec_emb = dec_emb_layer(decoder_inputs)\n",
    "\n",
    "decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True,dropout=0.4,recurrent_dropout=0.2)\n",
    "decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])\n",
    "\n",
    "#dense layer\n",
    "decoder_dense =  TimeDistributed(Dense(y_voc, activation='softmax'))\n",
    "decoder_outputs = decoder_dense(decoder_outputs)\n",
    "\n",
    "# Define the model \n",
    "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a53b8d4b",
   "metadata": {
    "id": "a53b8d4b"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "10cba72e",
   "metadata": {
    "id": "10cba72e"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d0a8bc3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6d0a8bc3",
    "outputId": "4a3c2ddb-8908-4d69-ed59-ee47ad8a8578"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90239 samples, validate on 10027 samples\n",
      "Epoch 1/2\n",
      " 1024/90239 [..............................] - ETA: 4:05 - loss: 3.3968 - acc: 0.5378"
     ]
    }
   ],
   "source": [
    "history=model.fit([x_tr,y_tr[:,:-1]], y_tr.reshape(y_tr.shape[0],y_tr.shape[1], 1)[:,1:] ,epochs=2,callbacks=[es],batch_size=256, validation_data=([x_val,y_val[:,:-1]], y_val.reshape(y_val.shape[0],y_val.shape[1], 1)[:,1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "94fee59e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "94fee59e",
    "outputId": "a547a2d2-f1a2-4c00-af82-b764fabedb3f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "61728d40",
   "metadata": {
    "id": "61728d40"
   },
   "outputs": [],
   "source": [
    "reverse_target_word_index=y_tokenizer.index_word\n",
    "reverse_source_word_index=x_tokenizer.index_word\n",
    "target_word_index=y_tokenizer.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a8li0qvg1bWL",
   "metadata": {
    "id": "a8li0qvg1bWL"
   },
   "outputs": [],
   "source": [
    "# Encode the input sequence to get the feature vector\n",
    "encoder_model = Model(inputs=encoder_inputs,outputs=[encoder_outputs, state_h, state_c])\n",
    "\n",
    "# Decoder setup\n",
    "# Below tensors will hold the states of the previous time step\n",
    "decoder_state_input_h = Input(shape=(latent_dim,))\n",
    "decoder_state_input_c = Input(shape=(latent_dim,))\n",
    "decoder_hidden_state_input = Input(shape=(max_text_len,latent_dim))\n",
    "\n",
    "# Get the embeddings of the decoder sequence\n",
    "dec_emb2= dec_emb_layer(decoder_inputs) \n",
    "# To predict the next word in the sequence, set the initial states to the states from the previous time step\n",
    "decoder_outputs2, state_h2, state_c2 = decoder_lstm(dec_emb2, initial_state=[decoder_state_input_h, decoder_state_input_c])\n",
    "\n",
    "# A dense softmax layer to generate prob dist. over the target vocabulary\n",
    "decoder_outputs2 = decoder_dense(decoder_outputs2) \n",
    "\n",
    "# Final decoder model\n",
    "decoder_model = Model(\n",
    "    [decoder_inputs] + [decoder_hidden_state_input,decoder_state_input_h, decoder_state_input_c],\n",
    "    [decoder_outputs2] + [state_h2, state_c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "AjZNuOcJ1cfj",
   "metadata": {
    "id": "AjZNuOcJ1cfj"
   },
   "outputs": [],
   "source": [
    "def decode_sequence(input_seq):\n",
    "    # Encode the input as state vectors.\n",
    "    e_out, e_h, e_c = encoder_model.predict(input_seq)\n",
    "    \n",
    "    # Generate empty target sequence of length 1.\n",
    "    target_seq = np.zeros((1,1))\n",
    "    \n",
    "    # Populate the first word of target sequence with the start word.\n",
    "    target_seq[0, 0] = target_word_index['sostok']\n",
    "\n",
    "    stop_condition = False\n",
    "    decoded_sentence = ''\n",
    "    while not stop_condition:\n",
    "      \n",
    "        output_tokens, h, c = decoder_model.predict([target_seq] + [e_out, e_h, e_c])\n",
    "\n",
    "        # Sample a token\n",
    "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
    "        sampled_token = reverse_target_word_index[sampled_token_index]\n",
    "        \n",
    "        if(sampled_token!='eostok'):\n",
    "            decoded_sentence += ' '+sampled_token\n",
    "\n",
    "        # Exit condition: either hit max length or find stop word.\n",
    "        if (sampled_token == 'eostok'  or len(decoded_sentence.split()) >= (max_summary_len-1)):\n",
    "            stop_condition = True\n",
    "\n",
    "        # Update the target sequence (of length 1).\n",
    "        target_seq = np.zeros((1,1))\n",
    "        target_seq[0, 0] = sampled_token_index\n",
    "\n",
    "        # Update internal states\n",
    "        e_h, e_c = h, c\n",
    "\n",
    "    return decoded_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6xtwG9do1h89",
   "metadata": {
    "id": "6xtwG9do1h89"
   },
   "outputs": [],
   "source": [
    "def seq2summary(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if((i!=0 and i!=target_word_index['sostok']) and i!=target_word_index['eostok']):\n",
    "            newString=newString+reverse_target_word_index[i]+' '\n",
    "    return newString\n",
    "\n",
    "def seq2text(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if(i!=0):\n",
    "            newString=newString+reverse_source_word_index[i]+' '\n",
    "    return newString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "NRu4lIgM1lkE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NRu4lIgM1lkE",
    "outputId": "58383988-4b84-4608-ddfe-3260953b76dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review: the wwe has confirmed the will return to the ring for the first time since wrestlemania in which he lost and left his iconic coat and hat in the sparking retirement he will return on flagship raw anniversary on january who retired in will also attend the \n",
      "Original summary: the to make wwe return on anniversary \n",
      "Predicted summary:  to be auctioned for first time\n",
      "\n",
      "\n",
      "Review: as many as three people were killed in riots after dera sacha sauda chief gurmeet ram rahim was convicted of rape by a cbi court on railway station and a petrol pump were set on fire amid clashes between security forces and the reports said over cars were set on \n",
      "Original summary: dead in riots after dera chief ram rahim convicted of rape \n",
      "Predicted summary:  people who killed in bihar shelter shelter home\n",
      "\n",
      "\n",
      "Review: bengaluru police has claimed they have solved cases after arresting a in the named had snatched gold chains of people in and around and had warrants pending against him from different police said they have recovered kg of gold ornaments worth crore and five motorbikes from \n",
      "Original summary: bengaluru police claims solving cases with one arrest \n",
      "Predicted summary:  police arrests over kg of bitcoin\n",
      "\n",
      "\n",
      "Review: around tonnes of meat sourced from animal carcasses has been seized from an ice factory in while three accused in the case said the meat was mostly of stray dogs and municipal authorities said it will be tested to confirm the the which was chemically treated at was to be sold to \n",
      "Original summary: meat from dog for sale seized in wb \n",
      "Predicted summary:  kg of kg seized in delhi\n",
      "\n",
      "\n",
      "Review: australia gave away five runs as penalty after a ball from nathan lyon hit the helmet on the final day of the fifth test in sydney on the ball slipped past wicketkeeper tim paine before rolling on to hit his helmet placed right behind at the australia were one wicket away from the \n",
      "Original summary: australia handed penalty as ball hits helmet \n",
      "Predicted summary:  player takes catch after being hit by ball\n",
      "\n",
      "\n",
      "Review: sharing an old poem on twitter which sushant singh rajput had written for his late he day sushant had earlier revealed the poem was his first ever attempt at in another sushant wish with all my could just be able to have one more minute with \n",
      "Original summary: sushant shares old poem he had written for his late mother \n",
      "Predicted summary:  sushant shares pic with his girlfriend\n",
      "\n",
      "\n",
      "Review: sports minister vijay goel conducted a surprise check at indira gandhi venue of the asian wrestling and cleaned the toilet himself after finding it visit came after international wrestlers reportedly complained about dirty saying they had to relieve themselves in the goel also pulled up sports authority of india officials at the \n",
      "Original summary: sports minister goel cleans toilet at wrestling venue \n",
      "Predicted summary:  minister gets married to remove at\n",
      "\n",
      "\n",
      "Review: there are no and shiv sena members in the list of new ministers who were inducted into the union cabinet on this was because the bjp leadership was unable to come up with a formula for representation which could satisfy all according to shiv sena has three members in the rajya while has \n",
      "Original summary: no shiv sena names in list of new union ministers \n",
      "Predicted summary:  shiv sena chief calls for\n",
      "\n",
      "\n",
      "Review: netflix on tuesday confirmed continue its association with anurag vikramaditya motwane and writer varun grover for season they added they carried out an independent investigation and are satisfied with the while varun was accused of sexual kashyap and motwane were criticised for their alleged inaction over allegations against their business partner vikas \n",
      "Original summary: netflix to continue working with anurag kashyap for sacred games \n",
      "Predicted summary:  sushant to replace kapil sharma\n",
      "\n",
      "\n",
      "Review: a group of labourers living at a makeshift settlement near on friday travelled over km to reach their respective polling stations to cast the labourers who otherwise travel in bullock carts to arranged for private vehicles to reach the polling have to shows my one of the labourers \n",
      "Original summary: labourers travel km to vote for telangana elections \n",
      "Predicted summary:  doctors remove from floor at mumbai\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    print(\"Review:\",seq2text(x_tr[i]))\n",
    "    print(\"Original summary:\",seq2summary(y_tr[i]))\n",
    "    print(\"Predicted summary:\",decode_sequence(x_tr[i].reshape(1,max_text_len)))\n",
    "    print(\"\\n\")"
   ]
  }
 ],
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