TestRank_Text_Summarization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TestRank_Text_Summarization.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfZQ0bWRJ2gk"
      },
      "source": [
        "# Text Summarization using text rank algorithm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtR7wm6qLKz0"
      },
      "source": [
        "# Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeptlGXN2MnF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dcda257-3ca4-47e2-ebeb-8de858376b90"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "nltk.download('punkt') # one time execution\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMtTRTALLO7n"
      },
      "source": [
        "# Uploading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_aSx78ZGpcN",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "5a74a86b-8638-4182-ca88-b19c39fa148f"
      },
      "source": [
        "# Upload the CSV file\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-047c5f21-426c-4f78-a231-59f13c38ff46\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-047c5f21-426c-4f78-a231-59f13c38ff46\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving tennis_articles_v4.csv to tennis_articles_v4.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5FvQ9LHGtja"
      },
      "source": [
        "# Read the CSV file\n",
        "import io\n",
        "df = pd.read_csv(io.StringIO(uploaded['tennis_articles_v4.csv'].decode(\"utf-8\")))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hm6pa5Af4qbE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c00c5c64-e5c9-4d25-d637-a7d08cf8181c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>article_id</th>\n",
              "      <th>article_text</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Maria Sharapova has basically no friends as te...</td>\n",
              "      <td>https://www.tennisworldusa.org/tennis/news/Mar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>BASEL, Switzerland (AP), Roger Federer advance...</td>\n",
              "      <td>http://www.tennis.com/pro-game/2018/10/copil-s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Roger Federer has revealed that organisers of ...</td>\n",
              "      <td>https://scroll.in/field/899938/tennis-roger-fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Kei Nishikori will try to end his long losing ...</td>\n",
              "      <td>http://www.tennis.com/pro-game/2018/10/nishiko...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Federer, 37, first broke through on tour over ...</td>\n",
              "      <td>https://www.express.co.uk/sport/tennis/1036101...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   article_id  ...                                             source\n",
              "0           1  ...  https://www.tennisworldusa.org/tennis/news/Mar...\n",
              "1           2  ...  http://www.tennis.com/pro-game/2018/10/copil-s...\n",
              "2           3  ...  https://scroll.in/field/899938/tennis-roger-fe...\n",
              "3           4  ...  http://www.tennis.com/pro-game/2018/10/nishiko...\n",
              "4           5  ...  https://www.express.co.uk/sport/tennis/1036101...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z66PfM1ILUOc"
      },
      "source": [
        "# Preprocessing the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZVoc3R6G9a8"
      },
      "source": [
        "# split the the text in the articles into sentences\n",
        "sentences = []\n",
        "for s in df['article_text']:\n",
        "  sentences.append(sent_tokenize(s))  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_lwimHsHB9l"
      },
      "source": [
        "# flatten the list\n",
        "sentences = [y for x in sentences for y in x]\n",
        "# for x in sentences:\n",
        "#   for y in x:\n",
        "#     sentences.append(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHvW8lKFHV1x"
      },
      "source": [
        "# remove punctuations, numbers and special characters\n",
        "clean_sentences = pd.Series(sentences).str.replace(\"[^a-zA-Z]\", \" \")\n",
        "\n",
        "# make alphabets lowercase\n",
        "clean_sentences = [s.lower() for s in clean_sentences]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPBZsdeWHlHf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ba74fc8-faf9-4c83-f7ff-7729fd267a3e"
      },
      "source": [
        "nltk.download('stopwords')# one time execution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKk_3HZ-Idjm"
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RX_NFApzIkmC"
      },
      "source": [
        "# function to remove stopwords\n",
        "def remove_stopwords(sen):\n",
        "  sen_new = \" \".join([i for i in sen if i not in stop_words])\n",
        "  return sen_new\n",
        "\n",
        "# for i in sen:\n",
        "#   if i not in stop_words:\n",
        "#     sen_new += i"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcFap_w9Ivob"
      },
      "source": [
        "# remove stopwords from the sentences\n",
        "clean_sentences = [remove_stopwords(r.split()) for r in clean_sentences]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEdr7y2SLbZJ"
      },
      "source": [
        "# Downloading the word embeddings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5Skjq6DJUtQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b00b7ab-9712-4e0a-d9f7-81a88ac6cba4"
      },
      "source": [
        "# download pretrained GloVe word embeddings\n",
        "! wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-12-18 04:48:50--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2020-12-18 04:48:50--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2020-12-18 04:48:50--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  2.07MB/s    in 7m 5s   \n",
            "\n",
            "2020-12-18 04:56:01 (1.94 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_GXHzwDJq-2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c6af747-853c-44cd-ccd3-7cf3c0021698"
      },
      "source": [
        "! unzip glove*.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFBkSZ17LkL3"
      },
      "source": [
        "# Extracting word vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsXIa7CBKsWQ"
      },
      "source": [
        "# Extract word vectors\n",
        "word_embeddings = {} # example {fox:.822455, .....}\n",
        "f = open('glove.6B.100d.txt', encoding='utf-8')\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    word_embeddings[word] = coefs\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OevCPR-GLpJf"
      },
      "source": [
        "# Formation of sentence vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3VtdSPyKxUZ"
      },
      "source": [
        "sentence_vectors = []\n",
        "for i in clean_sentences:\n",
        "  if len(i) != 0:\n",
        "    v = sum([word_embeddings.get(w, np.zeros((100,))) for w in i.split()])/(len(i.split())+0.001)\n",
        "  else:\n",
        "    v = np.zeros((100,))\n",
        "  sentence_vectors.append(v)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3Iww3I9LYhJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "406a9783-3504-4974-b75d-0cd356ef34d9"
      },
      "source": [
        "len(sentence_vectors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "119"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bh9L2pqL3gp"
      },
      "source": [
        "# Finding similarities by using cosine similarity\r\n",
        "The next step is to find similarities among the sentences. We will use cosine similarity to find similarity between a pair of sentences. Let's create an empty similarity matrix for this task and populate it with cosine similarities of the sentences."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mm_fNZpOLxbM"
      },
      "source": [
        "# similarity matrix\n",
        "sim_mat = np.zeros([len(sentences), len(sentences)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVeHkvf0MO1j"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTAAe-q3L4xM"
      },
      "source": [
        "for i in range(len(sentences)):\n",
        "  for j in range(len(sentences)):\n",
        "    if i != j:\n",
        "      sim_mat[i][j] = cosine_similarity(sentence_vectors[i].reshape(1,100), sentence_vectors[j].reshape(1,100))[0,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i14xr6HeL63Y"
      },
      "source": [
        "# Forming Graph from similarity Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAQUnNRWL_tA"
      },
      "source": [
        "import networkx as nx\n",
        "\n",
        "nx_graph = nx.from_numpy_array(sim_mat)\n",
        "scores = nx.pagerank(nx_graph)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ze9zC0BpMDjP"
      },
      "source": [
        "# Sorting and printing Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQCcvT3yO5Xj"
      },
      "source": [
        "ranked_sentences = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwxtPBlgO_Gk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ecb2101-2fb4-4adc-f6eb-3d9e2b8875b7"
      },
      "source": [
        "# Specify number of sentences to form the summary\n",
        "sn = 10\n",
        "\n",
        "# Generate summary\n",
        "for i in range(sn):\n",
        "  print(f\"{i}.{ranked_sentences[i][1]}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "When I'm on the courts or when I'm on the court playing, I'm a competitor and I want to beat every single person whether they're in the locker room or across the net.So I'm not the one to strike up a conversation about the weather and know that in the next few minutes I have to go and try to win a tennis match.\n",
            "Major players feel that a big event in late November combined with one in January before the Australian Open will mean too much tennis and too little rest.\n",
            "Speaking at the Swiss Indoors tournament where he will play in Sundays final against Romanian qualifier Marius Copil, the world number three said that given the impossibly short time frame to make a decision, he opted out of any commitment.\n",
            "\"I felt like the best weeks that I had to get to know players when I was playing were the Fed Cup weeks or the Olympic weeks, not necessarily during the tournaments.\n",
            "Currently in ninth place, Nishikori with a win could move to within 125 points of the cut for the eight-man event in London next month.\n",
            "He used his first break point to close out the first set before going up 3-0 in the second and wrapping up the win on his first match point.\n",
            "The Spaniard broke Anderson twice in the second but didn't get another chance on the South African's serve in the final set.\n",
            "\"We also had the impression that at this stage it might be better to play matches than to train.\n",
            "The competition is set to feature 18 countries in the November 18-24 finals in Madrid next year, and will replace the classic home-and-away ties played four times per year for decades.\n",
            "Federer said earlier this month in Shanghai in that his chances of playing the Davis Cup were all but non-existent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fc1JXVdMMJqX"
      },
      "source": [
        "# End"
      ]
    }
  ]
}