Added models and notebooks

rr_segmentation.h5

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+oid sha256:d849b086b26ed0a3beeacd52795c794b4081f1ba823c23a8b9bf315a9f80ae26
+size 44276936

summarization.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4ee65aded446bd4621dd6c31f118c65b64ec18c6e3f60648e8edea6cc8fc930
+size 44276936

summarization.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e028a78e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:33:45.203715Z",
+     "iopub.status.busy": "2024-10-26T12:33:45.203302Z",
+     "iopub.status.idle": "2024-10-26T12:34:04.776829Z",
+     "shell.execute_reply": "2024-10-26T12:34:04.775630Z"
+    },
+    "id": "y73ZVTnK0YCQ",
+    "papermill": {
+     "duration": 19.582191,
+     "end_time": "2024-10-26T12:34:04.779343",
+     "exception": false,
+     "start_time": "2024-10-26T12:33:45.197152",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install rouge sumy rouge_score datasets -qq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "efb01005",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:04.791156Z",
+     "iopub.status.busy": "2024-10-26T12:34:04.790818Z",
+     "iopub.status.idle": "2024-10-26T12:34:08.386003Z",
+     "shell.execute_reply": "2024-10-26T12:34:08.385203Z"
+    },
+    "id": "NBHSsTcyyoef",
+    "papermill": {
+     "duration": 3.603978,
+     "end_time": "2024-10-26T12:34:08.388454",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:04.784476",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "import nltk\n",
+    "from rouge import Rouge\n",
+    "from datasets import load_dataset\n",
+    "from sumy.nlp.stemmers import Stemmer\n",
+    "from sumy.nlp.tokenizers import Tokenizer\n",
+    "from sumy.parsers.plaintext import PlaintextParser\n",
+    "from sumy.summarizers.kl import KLSummarizer\n",
+    "from sumy.summarizers.lsa import LsaSummarizer\n",
+    "from sumy.summarizers.lex_rank import LexRankSummarizer\n",
+    "from sumy.summarizers.sum_basic import SumBasicSummarizer\n",
+    "from sumy.summarizers.text_rank import TextRankSummarizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b0b7c513",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:08.399571Z",
+     "iopub.status.busy": "2024-10-26T12:34:08.399079Z",
+     "iopub.status.idle": "2024-10-26T12:34:11.926694Z",
+     "shell.execute_reply": "2024-10-26T12:34:11.925741Z"
+    },
+    "id": "myJUj_R7-uXL",
+    "papermill": {
+     "duration": 3.535373,
+     "end_time": "2024-10-26T12:34:11.928820",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:08.393447",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99e5ef8ff5bc4ee8a84127dd82efe116",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/368 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "924cf9a0d563431d81521baf2078f8d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "train.csv:   0%|          | 0.00/36.5M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce22f82394154ee383873818bf330908",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "test.csv:   0%|          | 0.00/17.7M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e890c18d50c44f978c10fcbfaa9d7f11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/2058 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f005554d55e47e2ba443dc891d4b0e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split:   0%|          | 0/1015 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset = load_dataset(\"d0r1h/ILC\", split='test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eedac49d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:11.942831Z",
+     "iopub.status.busy": "2024-10-26T12:34:11.942469Z",
+     "iopub.status.idle": "2024-10-26T12:34:11.949195Z",
+     "shell.execute_reply": "2024-10-26T12:34:11.948272Z"
+    },
+    "id": "RYJyqp-Z-5hV",
+    "outputId": "fd79fe6a-9c5a-4f11-995b-a5a19640d8d3",
+    "papermill": {
+     "duration": 0.016454,
+     "end_time": "2024-10-26T12:34:11.951249",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:11.934795",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['Title', 'Summary', 'Case'],\n",
+       "    num_rows: 1015\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "33809db2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:11.964246Z",
+     "iopub.status.busy": "2024-10-26T12:34:11.963924Z",
+     "iopub.status.idle": "2024-10-26T12:34:12.048940Z",
+     "shell.execute_reply": "2024-10-26T12:34:12.048147Z"
+    },
+    "id": "J4rmm7voyoUk",
+    "outputId": "a986658d-d987-4475-dd7e-63c7087d7b2f",
+    "papermill": {
+     "duration": 0.094143,
+     "end_time": "2024-10-26T12:34:12.051285",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:11.957142",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(dataset)\n",
+    "\n",
+    "df= df.sample(300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23baf647",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:12.065263Z",
+     "iopub.status.busy": "2024-10-26T12:34:12.064923Z",
+     "iopub.status.idle": "2024-10-26T12:34:12.208359Z",
+     "shell.execute_reply": "2024-10-26T12:34:12.207406Z"
+    },
+    "id": "Kle1TB8pyoDb",
+    "outputId": "3fce24d0-dcfd-4756-d352-48c366ea1155",
+    "papermill": {
+     "duration": 0.152826,
+     "end_time": "2024-10-26T12:34:12.210670",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:12.057844",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "nltk.download(\"punkt\")\n",
+    "rouge = Rouge()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "359b61e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:12.224138Z",
+     "iopub.status.busy": "2024-10-26T12:34:12.223352Z",
+     "iopub.status.idle": "2024-10-26T12:34:12.228392Z",
+     "shell.execute_reply": "2024-10-26T12:34:12.227508Z"
+    },
+    "id": "SRkBvDPnyoBg",
+    "outputId": "43424be1-a0a2-4642-c64d-f214b1dcef3a",
+    "papermill": {
+     "duration": 0.014117,
+     "end_time": "2024-10-26T12:34:12.230610",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:12.216493",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3000\n",
+      "10000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sys.getrecursionlimit())\n",
+    "sys.setrecursionlimit(10000)\n",
+    "print(sys.getrecursionlimit())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d0245dcc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:12.243768Z",
+     "iopub.status.busy": "2024-10-26T12:34:12.243205Z",
+     "iopub.status.idle": "2024-10-26T12:34:12.248590Z",
+     "shell.execute_reply": "2024-10-26T12:34:12.247753Z"
+    },
+    "id": "O5Jk5LR1yn_i",
+    "papermill": {
+     "duration": 0.014078,
+     "end_time": "2024-10-26T12:34:12.250596",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:12.236518",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def summarize(text, sumarizer, SENTENCES_COUNT):\n",
+    "    sentences_ = []\n",
+    "    doc = text\n",
+    "    doc_ = PlaintextParser(doc, Tokenizer(\"en\")).document\n",
+    "    for sentence in sumarizer(doc_, SENTENCES_COUNT):\n",
+    "        sentences_.append(str(sentence))\n",
+    "\n",
+    "    summm_ = \" \".join(sentences_)\n",
+    "    return summm_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4cf755a5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:34:12.263278Z",
+     "iopub.status.busy": "2024-10-26T12:34:12.263001Z",
+     "iopub.status.idle": "2024-10-26T12:51:43.519475Z",
+     "shell.execute_reply": "2024-10-26T12:51:43.517884Z"
+    },
+    "id": "ZDm1gEE1yn7p",
+    "papermill": {
+     "duration": 1051.267032,
+     "end_time": "2024-10-26T12:51:43.523381",
+     "exception": false,
+     "start_time": "2024-10-26T12:34:12.256349",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df[\"LexRankSummary\"] = df[\"Case\"].map(\n",
+    "    lambda x: summarize(x, LexRankSummarizer(), 3)\n",
+    ")\n",
+    "df[\"KLSummary\"] = df[\"Case\"].map(\n",
+    "    lambda x: summarize(x, KLSummarizer(), 3)\n",
+    ")\n",
+    "df[\"TextRankSummary\"] = df[\"Case\"].map(\n",
+    "    lambda x: summarize(x, TextRankSummarizer(), 3)\n",
+    ")\n",
+    "df[\"SumBasicSummary\"] = df[\"Case\"].map(\n",
+    "    lambda x: summarize(x, SumBasicSummarizer(), 3)\n",
+    ")\n",
+    "df[\"LsaSummary\"] = df[\"Case\"].map(\n",
+    "    lambda x: summarize(x, LsaSummarizer(), 3)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "de7283ad",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:51:43.551606Z",
+     "iopub.status.busy": "2024-10-26T12:51:43.551001Z",
+     "iopub.status.idle": "2024-10-26T12:51:43.590519Z",
+     "shell.execute_reply": "2024-10-26T12:51:43.589141Z"
+    },
+    "id": "T1XLZsDzyn1_",
+    "outputId": "418f6d0a-2ebf-4ecc-9050-929720c12764",
+    "papermill": {
+     "duration": 0.057838,
+     "end_time": "2024-10-26T12:51:43.594515",
+     "exception": false,
+     "start_time": "2024-10-26T12:51:43.536677",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>Summary</th>\n",
+       "      <th>LexRankSummary</th>\n",
+       "      <th>KLSummary</th>\n",
+       "      <th>TextRankSummary</th>\n",
+       "      <th>SumBasicSummary</th>\n",
+       "      <th>LsaSummary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>When the wife is being physically as well as m...</td>\n",
+       "      <td>It is to be noted that the appellant married t...</td>\n",
+       "      <td>7. 8. r n s5 7 https: www.mhc.tn.gov.in judis ...</td>\n",
+       "      <td>The case of the prosecution is that the appell...</td>\n",
+       "      <td>The case of the prosecution is that the appell...</td>\n",
+       "      <td>Crl.A.No.1821IN THE HIGH COURT OF JUDICATURE A...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>Courts are not allowed to issue orders on a sy...</td>\n",
+       "      <td>They were admitted in 1st year Professional MB...</td>\n",
+       "      <td>However Glocal Medical College did not conduct...</td>\n",
+       "      <td>They were admitted in 1st year Professional MB...</td>\n",
+       "      <td>411 of 2017. The review petitioners of 2016. I...</td>\n",
+       "      <td>Nos 31037­31038 of 2016 may appear in the exam...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>The man alleged of causing injury to the infor...</td>\n",
+       "      <td>Learned counsel submitted that the petitioner ...</td>\n",
+       "      <td>Sikandra District ... Petitioner s The State o...</td>\n",
+       "      <td>Having considered the facts and circumstances ...</td>\n",
+       "      <td>Further it was submitted that only Upendra Kum...</td>\n",
+       "      <td>The petitioner apprehends arrest in connection...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Summary  \\\n",
+       "294  When the wife is being physically as well as m...   \n",
+       "616  Courts are not allowed to issue orders on a sy...   \n",
+       "659  The man alleged of causing injury to the infor...   \n",
+       "\n",
+       "                                        LexRankSummary  \\\n",
+       "294  It is to be noted that the appellant married t...   \n",
+       "616  They were admitted in 1st year Professional MB...   \n",
+       "659  Learned counsel submitted that the petitioner ...   \n",
+       "\n",
+       "                                             KLSummary  \\\n",
+       "294  7. 8. r n s5 7 https: www.mhc.tn.gov.in judis ...   \n",
+       "616  However Glocal Medical College did not conduct...   \n",
+       "659  Sikandra District ... Petitioner s The State o...   \n",
+       "\n",
+       "                                       TextRankSummary  \\\n",
+       "294  The case of the prosecution is that the appell...   \n",
+       "616  They were admitted in 1st year Professional MB...   \n",
+       "659  Having considered the facts and circumstances ...   \n",
+       "\n",
+       "                                       SumBasicSummary  \\\n",
+       "294  The case of the prosecution is that the appell...   \n",
+       "616  411 of 2017. The review petitioners of 2016. I...   \n",
+       "659  Further it was submitted that only Upendra Kum...   \n",
+       "\n",
+       "                                            LsaSummary  \n",
+       "294  Crl.A.No.1821IN THE HIGH COURT OF JUDICATURE A...  \n",
+       "616  Nos 31037­31038 of 2016 may appear in the exam...  \n",
+       "659  The petitioner apprehends arrest in connection...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Summary','LexRankSummary','KLSummary','TextRankSummary','SumBasicSummary','LsaSummary']].sample(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6adcfe6e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:51:43.625810Z",
+     "iopub.status.busy": "2024-10-26T12:51:43.625164Z",
+     "iopub.status.idle": "2024-10-26T12:51:43.742764Z",
+     "shell.execute_reply": "2024-10-26T12:51:43.741676Z"
+    },
+    "papermill": {
+     "duration": 0.1356,
+     "end_time": "2024-10-26T12:51:43.745129",
+     "exception": false,
+     "start_time": "2024-10-26T12:51:43.609529",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_path = \"/kaggle/working/\"\n",
+    "\n",
+    "df[['Summary','LexRankSummary','KLSummary','TextRankSummary','SumBasicSummary','LsaSummary']].to_csv(data_path + \"Extractiveprediction3.csv\", index=False, header=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "421155db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-26T12:51:43.759471Z",
+     "iopub.status.busy": "2024-10-26T12:51:43.759154Z",
+     "iopub.status.idle": "2024-10-26T12:51:55.688817Z",
+     "shell.execute_reply": "2024-10-26T12:51:55.687756Z"
+    },
+    "papermill": {
+     "duration": 11.939564,
+     "end_time": "2024-10-26T12:51:55.691335",
+     "exception": false,
+     "start_time": "2024-10-26T12:51:43.751771",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: rouge-score in /opt/conda/lib/python3.10/site-packages (0.1.2)\r\n",
+      "Requirement already satisfied: absl-py in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.4.0)\r\n",
+      "Requirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (from rouge-score) (3.2.4)\r\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.26.4)\r\n",
+      "Requirement already satisfied: six>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.16.0)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install rouge-score"
+   ]
+  },
+  {
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+   "execution_count": 13,
+   "id": "2453c693",
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "ROUGE scores for LexRankSummary:\n",
+      "        Precision     Recall   F1-Score\n",
+      "rouge1  70.769953  29.569133  39.050146\n",
+      "rouge2  39.292266  15.107604  20.437364\n",
+      "rougeL  44.476200  17.208033  23.224665\n",
+      "\n",
+      "ROUGE scores for KLSummary:\n",
+      "        Precision     Recall   F1-Score\n",
+      "rouge1  77.686073  12.652439  21.109943\n",
+      "rouge2  44.456896   6.802124  11.454205\n",
+      "rougeL  56.885144   8.869685  14.897441\n",
+      "\n",
+      "ROUGE scores for TextRankSummary:\n",
+      "        Precision     Recall   F1-Score\n",
+      "rouge1  66.013084  36.161190  44.008815\n",
+      "rouge2  35.477692  17.981091  22.438805\n",
+      "rougeL  39.970523  20.464987  25.443410\n",
+      "\n",
+      "ROUGE scores for SumBasicSummary:\n",
+      "        Precision    Recall   F1-Score\n",
+      "rouge1  73.130813  6.938967  12.240295\n",
+      "rouge2  37.372772  3.533873   6.234627\n",
+      "rougeL  56.871362  5.156887   9.144979\n",
+      "\n",
+      "ROUGE scores for LsaSummary:\n",
+      "        Precision     Recall   F1-Score\n",
+      "rouge1  63.112133  10.959156  17.643400\n",
+      "rouge2  29.862840   5.111493   8.168334\n",
+      "rougeL  42.972665   7.269147  11.716963\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from rouge_score import rouge_scorer\n",
+    "\n",
+    "# Load dataset\n",
+    "data_path = \"/kaggle/working/\"\n",
+    "df = pd.read_csv(data_path + \"Extractiveprediction3.csv\")\n",
+    "\n",
+    "summary_columns = [\"LexRankSummary\", \"KLSummary\", \"TextRankSummary\", \"SumBasicSummary\", \"LsaSummary\"]\n",
+    "\n",
+    "def calculate_rouge_for_all_summaries(df, reference_column=\"Summary\", summary_columns=summary_columns):\n",
+    "    results = {}\n",
+    "    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
+    "\n",
+    "    for col in summary_columns:\n",
+    "        summaries = pd.DataFrame(list(zip(df[reference_column], df[col])), columns=['GoldSummary', 'SystemSummary'])\n",
+    "\n",
+    "        scores = summaries.apply(lambda row: scorer.score(row['GoldSummary'], row['SystemSummary']), axis=1)\n",
+    "\n",
+    "        avg_scores = {}\n",
+    "        for rouge_type in scores[0].keys():\n",
+    "            avg_scores[rouge_type] = {\n",
+    "                \"precision\": sum(score[rouge_type].precision for score in scores) / len(scores),\n",
+    "                \"recall\": sum(score[rouge_type].recall for score in scores) / len(scores),\n",
+    "                \"fmeasure\": sum(score[rouge_type].fmeasure for score in scores) / len(scores)\n",
+    "            }\n",
+    "        \n",
+    "        results[col] = pd.DataFrame(avg_scores).T * 100  \n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "rouge_scores = calculate_rouge_for_all_summaries(df)\n",
+    "for method, scores_df in rouge_scores.items():\n",
+    "    print(f\"\\nROUGE scores for {method}:\")\n",
+    "    print(scores_df.rename(columns={'precision': 'Precision', 'recall': 'Recall', 'fmeasure': 'F1-Score'}))\n"
+   ]
+  },
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