{ "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\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SummaryLexRankSummaryKLSummaryTextRankSummarySumBasicSummaryLsaSummary
294When the wife is being physically as well as m...It is to be noted that the appellant married t...7. 8. r n s5 7 https: www.mhc.tn.gov.in judis ...The case of the prosecution is that the appell...The case of the prosecution is that the appell...Crl.A.No.1821IN THE HIGH COURT OF JUDICATURE A...
616Courts are not allowed to issue orders on a sy...They were admitted in 1st year Professional MB...However Glocal Medical College did not conduct...They were admitted in 1st year Professional MB...411 of 2017. The review petitioners of 2016. I...Nos 31037­31038 of 2016 may appear in the exam...
659The man alleged of causing injury to the infor...Learned counsel submitted that the petitioner ...Sikandra District ... Petitioner s The State o...Having considered the facts and circumstances ...Further it was submitted that only Upendra Kum...The petitioner apprehends arrest in connection...
\n", "" ], "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" ] }, { "cell_type": "code", "execution_count": 13, "id": "2453c693", "metadata": { "execution": { "iopub.execute_input": "2024-10-26T12:51:55.705796Z", "iopub.status.busy": "2024-10-26T12:51:55.705150Z", "iopub.status.idle": "2024-10-26T12:53:44.896029Z", "shell.execute_reply": "2024-10-26T12:53:44.895097Z" }, "id": "xLHPIvqMnFYQ", "papermill": { "duration": 109.207268, "end_time": "2024-10-26T12:53:44.904888", "exception": false, "start_time": "2024-10-26T12:51:55.697620", "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" ] }, { "cell_type": "code", "execution_count": null, "id": "a871e738", "metadata": { "papermill": { "duration": 0.005759, "end_time": "2024-10-26T12:53:44.917152", "exception": false, "start_time": "2024-10-26T12:53:44.911393", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Extractive.ipynb", "provenance": [] }, "kaggle": { "accelerator": "gpu", "dataSources": [], "dockerImageVersionId": 30787, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", 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